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What Is Conjoint Analysis & How Can You Use It?

• 18 Dec 2020

For a business to run effectively, its leadership needs a firm understanding of the value its products or services bring to consumers. This understanding allows for a more informed strategy across the board—from long-term planning to pricing and sales.

In today’s business environment, most products and services include multiple features and functions by default. So, how do businesses go about learning which ones their customers value most? Is it possible to assign a specific value to each feature a product offers?

This is where conjoint analysis becomes an essential tool.

Here’s an overview of conjoint analysis, why it’s important, and steps you can take to analyze your products or services.

Access your free e-book today.

What Is Conjoint Analysis?

Conjoint analysis is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services. It’s based on the principle that any product can be broken down into a set of attributes that ultimately impact users’ perceived value of an item or service.

Conjoint analysis is typically conducted via a specialized survey that asks consumers to rank the importance of the specific features in question. Analyzing the results allows the firm to then assign a value to each one.

Learn about conjoint analysis in the video below, and subscribe to our YouTube channel for more explainer content!

Types of Conjoint Analysis

Conjoint analysis can take various forms. Some of the most common include:

• Choice-Based Conjoint (CBC) Analysis: This is one of the most common forms of conjoint analysis and is used to identify how a respondent values combinations of features.
• Adaptive Conjoint Analysis (ACA): This form of analysis customizes each respondent's survey experience based on their answers to early questions. It’s often leveraged in studies where several features or attributes are being evaluated to streamline the process and extract the most valuable insights from each respondent.
• Full-Profile Conjoint Analysis: This form of analysis presents the respondent with a series of full product descriptions and asks them to select the one they’d be most inclined to buy.
• MaxDiff Conjoint Analysis: This form of analysis presents multiple options to the respondent, which they’re asked to organize on a scale of “best” to “worst” (or “most likely to buy” to “least likely to buy”).

The type of conjoint analysis a company uses is determined by the goals driving its analysis (i.e., what does it hope to learn?) and, potentially, the type of product or service being evaluated. It’s possible to combine multiple conjoint analysis types into “hybrid models” to take advantage of the benefits of each.

What Is Conjoint Analysis Used For?

The insights a company gleans from conjoint analysis of its product features can be leveraged in several ways. Most often, conjoint analysis impacts pricing strategy, sales and marketing efforts, and research and development plans.

Conjoint Analysis in Pricing

Conjoint analysis works by asking users to directly compare different features to determine how they value each one. When a company understands how its customers value its products or services’ features, it can use the information to develop its pricing strategy.

For example, a software company hoping to take advantage of network effects to scale its business might pursue a “freemium” model wherein its users access its product at no charge. If the company determines through conjoint analysis that its users highly value one feature above the others, it might choose to place that feature behind a paywall.

As such, conjoint analysis is an excellent means of understanding what product attributes determine a customer’s willingness to pay . It’s a method of learning what features a customer is willing to pay for and whether they’d be willing to pay more.

Conjoint Analysis in Sales & Marketing

Conjoint analysis can inform more than just a company’s pricing strategy; it can also inform how it markets and sells its offerings. When a company knows which features its customers value most, it can lean into them in its advertisements, marketing copy, and promotions.

On the other hand, a company may find that its customers aren’t uniform in assigning value to different features. In such a case, conjoint analysis can be a powerful means of segmenting customers based on their interests and how they value features—allowing for more targeted communication.

For example, an online store selling chocolate may find through conjoint analysis that its customers primarily value two features: Quality and the fact that a portion of each sale goes toward funding environmental sustainability efforts. The company can then use that information to send different messaging and appeal to each segment's specific value.

Conjoint Analysis in Research & Development

Conjoint analysis can also inform a company’s research and development pipeline. The insights gleaned can help determine which new features are added to its products or services, along with whether there’s enough market demand for an entirely new product.

For example, consider a smartphone manufacturer that conducts a conjoint analysis and discovers its customers value larger screens over all other features. With this information, the company might logically conclude that the best use of its product development budget and resources would be to develop larger screens. If, however, future analyses reveal that customer value has shifted to a different feature—for example, audio quality—the company may use that information to pivot its product development plans.

Additionally, a company may use conjoint analysis to narrow down its product or service’s features. Returning to the smartphone example: There’s only so much space within a smartphone for components. How a phone manufacturer’s customers value different features can inform which components make it into the end product—and which are cut.

One example is Apple’s 2016 decision to remove the headphone jack from the iPhone to free up space for other components. It’s reasonable to assume this decision was reached after analysis revealed that customers valued other features above a headphone jack.

Leveraging Conjoint Analysis for Your Business

Conjoint analysis is an incredibly useful tool you can leverage at your company. By using it to understand which product or service features your customers value over others, you can make more informed decisions about pricing, product development, and sales and marketing activities.

Are you interested in learning more about how customers perceive and realize value from the products they buy, and how you can use that information to better inform your business? Explore Economics for Managers — one of our online strategy courses —and download our free e-book on how to formulate a successful business strategy.

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What is Conjoint Analysis?

Conjoint analysis is a popular method of product and pricing research that uncovers consumers’ preferences, which is useful when a company wants to:

• Select product features.
• Assess consumers’ sensitivity to price changes.
• Forecast its volumes and market share.
• Predict adoption of new products or services.

Conjoint analysis is frequently used across different industries for all types of products, such as consumer goods, electrical goods, life insurance plans, retirement housing, luxury goods, and air travel. It is applicable in various instances that centre around discovering what type of product consumers are likely to buy and what consumers value the most (and least) about a product. As such, it is a familiar tool for marketers, product managers, and pricing specialists.

Businesses of all sizes can benefit from conjoint analysis, including even local grocery stores and restaurants — and its scope is not just limited to consumer contexts, for example, charities can use conjoint analysis’ techniques to find out donor preferences, while HR departments can use it to build optimal compensation packages .

How does conjoint analysis work?

Conjoint analysis works by breaking a product or service down into its components ( attributes and levels ) and testing different combinations of these components to identify consumer preferences .

For example, consider a conjoint study on smartphones. The smartphone is broken down into four attributes which are each assigned different possible variations to create levels:

Each choice task then presents a respondent with different possible smartphones, each created by combining different levels for each attribute:

Going further than simply asking respondents what they like in a product, or what features they find most important, conjoint analysis employs a more realistic approach: asking each respondent to choose between potential product concepts (or alternatives) formed through the combination of attributes and levels. These combinations are carefully assembled into choice sets (or questions). Each respondent is typically presented with 8 to 12 questions . The process of assembling attributes and levels into product concepts and then into choice sets is called experimental design and requires extensive statistical and mathematical analysis (done automatically by Conjointly or manually by researchers).

Using survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a “ preference score ” (AKA “partworth utility” or “utility score”). The below example shows preference scores for attributes and levels of a mobile phone plan.

Preference scores are used to build simulators that forecast market shares for a set of different products offered to the market. By using the simulator to model (i.e. simulate ) respondents’ decisions, we can identify the specific features and pricing that balance value to the customer with cost to the company and forecast potential demand in a competitive market situation. The below example shows how different data amounts in a mobile plan will affect a company’s market share.

Consider you are launching a new product and wish to address several research questions. Through the below example, we demonstrate how various outputs from your Conjointly survey report can be used to gain insights.

• It is also possible to perform clustering based on raw conjoint utilities .

Why do conjoint analysis with Conjointly?

Conjointly automates the often complicated experimental design process using state-of-the-art methodology. This gives you control over specific settings , such as the number of concepts per choice set and the number of choice sets per respondent when you set up a conjoint analysis experiment. Respondents then complete the choice tasks within the conjoint survey – this typically requires a few hundred responses but may vary depending on the complexity of the study.

Once we’ve gathered the recommended sample size of respondents, Conjointly produces a survey report which contains several in-depth outputs. The outputs of Brand Specific Conjoint , Generic Conjoint , and Brand-Price Trade-Off include estimates of respondents’ preferences, overall sample profile, segmentation and interactive simulations. Conjointly estimates and charts preference shares, revenue projections, and price elasticity using simulators.

There are many types/flavours of conjoint analysis , classified by response type, questioning approach, design type, and adaptivity of the design. All flavours of conjoint analysis have the same basics but not all are as effective as others. That’s why Conjointly offers two key conjoint designs, called generic and brand-specific, and uses the most tested, developed, and theoretically sound response type – choice-based conjoint analysis (CBC). CBC’s predictive power far surpasses its alternatives , such as SIMALTO and self-explicated conjoint, making it the ideal choice for your next experiment.

Don’t have a large marketing budget or the scope to conduct conjoint analysis? That’s OK: Conjointly does full conjoint analysis for you, affordably . Unlike desktop software tools, Conjointly does not require you to deep dive into the advanced methodology of conjoint analysis. Your business can rely on the full functionality of the software to deliver high-quality analysis and powerfully accurate results. Conjointly embodies an agile approach that puts you in control of the research process without the need.

Conjointly is made unique by the following characteristics:

We are the home of conjoint analysis. Conjointly offers complete set of outputs and features through an accessible interface.

Quick to set up. Setting up your experiment is fast and hassle-free with a simple wizard, which helps you choose appropriate settings and suggests your minimum sample size. You won’t need to customise or test any survey – the system does that for you. Conjointly can send participants invites on your behalf or generate a shareable link for you.

Easy on respondents. Experiment participants only need a few minutes to complete a survey and can answer questions with ease on their mobile phone, tablet, or computer.

Smart analytics done for you. Behind the scenes, Conjointly uses state-of-the-art analytics to crunch the numbers, and check validity of reporting. Outputs are ready for any application of conjoint analysis (pricing, feature selection, product testing, new market entry, cannibalisation analysis, etc.) in any industry (telecommunications, SaaS, FMCG, automotive, financial services, HR, etc.).

Our market research experts are always ready to support your studies. Schedule a consultation if you need any assistance.

What is the difference between conjoint and discrete choice experiments?

Conjoint analysis is a survey-based quantitative research technique of presenting respondents with several options (each described in terms of feature and price levels) and measuring their response to these options.

When the measured response is their choice between these options (rather than ranking or rating each of these options), it is called choice-based conjoint (which is the most commonly-used type of discrete choice experiments).

Discrete choice analysis is examination of datasets that contain choices made by people from among several alternatives. Commonly, we want to understand what drove people to make these choices. For example, how does weather affect people’s choice of eating out, ordering food delivery, or cooking at home. Discrete choice analysis can be done on historical data (e.g. sales data) or from experiments (including survey-based experiments).

Choice-based conjoint is an example of discrete choice experimentation.

History of conjoint analysis

Conjoint analysis has its roots in academic research from the 1960s and has been used commercially since the 1970s. In 1964, two mathematicians, Duncan Luce and John Tukey published a rather indigestible (by modern standards) article called ‘Simultaneous conjoint measurement: A new type of fundamental measurement’ . In abstract terms, they sketched the idea of “measuring the intrinsic goodness of certain characteristics of objects by measuring the goodness of an object as a whole”.

The article did not mention data collection, products, features, prices, or other elements that we associate with conjoint analysis today, but it spurred academic interest in the topic and perhaps gave rise to the name “conjoint”. It not only kick-started the topic but also set the tone for future developments in the area. Over time, it has become technical to the point of inaccessibility to most people, led by American academics with a strong emphasis on the statistical workings of survey research.

Green and Srinivasin (1978) agree that the theory of conjoint measurement was developed in Luce and Tukey’s paper but that “the first detailed, consumer-orientated” approach was Green and Rao’s (1971) ‘Conjoint Measurement for Quantifying Judgmental Data’ . In 1974, Professor Paul E. Green penned ‘On the Design of Choice Experiments Involving Multifactor Alternatives’ , cementing the impact of conjoint analysis in market research.

Over the next few decades, conjoint analysis became an increasingly popular method across the globe with notable studies in the 1980s and 90s highlighting its growing adoption and development during this time (Wittink & Cattin 1989; Wittink, Vriens, and Burhenne 1994 cited in Green, Kreiger & Wind 2001) .

Conjoint surveys are continuously developing on a range of software platforms, through which many different flavours of conjoint analysis can be enjoyed. Today, conjoint analysis thrives as a widespread tool built on a robust methodology and is used by market researchers daily as an indispensable tool for understanding consumer trade-offs.

Example outputs of Generic Conjoint on ice-cream

This is a simple conjoint analysis report for a Generic Conjoint test on ice-cream. You can also take this survey yourself . We tested three features:

• Flavour (Fudge, Vanilla, Strawberry, and Mango)
• Size (from 120g to 200g)
• Price (from $1.95 to $3.50)

We collected over 1,500 good quality responses in this test (even though this report would be robust enough with a hundred complete answers). It turns out that variation of price was a more important driver of people’s decision-making than differences in both flavour and size of the cone combined:

Unsurprisingly, people preferred larger and cheaper cones. Fudge and vanilla were the two top flavours:

But when we look at confidence intervals, we notice that we are much less certain about average preferences for flavours than for size or price:

It is probably because if we simulate preference shares for four concepts with varied flavours but fixed price and size, we observe that the distribution of people who pick different options is not extremely skewed towards one flavour:

But when we do simulation analysis with different price points, we clearly see that more people prefer to pay a lower price. Even though some still stick with a higher price, probably due to price-quality inference.

Another useful output of the study is marginal willingness to pay , which shows the equivalent amount of money for upgrade from the less preferred to the more preferred features:

If you want to pick the topmost preferred combination of product features, you can take a look at the following ranking as well:

It looks like a large dollop of modestly-priced Frosty Vanilla is the winner today.

A simple conjoint analysis example in Excel

To further your understanding, you can download a conjoint analysis example in Excel , also available on Google Sheets (which you can copy to edit). This example covers:

• Inputs for a conjoint study
• Questions presented to respondents
• Calculations of preference scores (relative preferences and importance scores of attributes)

This example is limited to:

• Ten choice-based responses (in real conjoint tests, we collect ~12 choices from 100 to 2,000 respondents);
• Four attributes with two levels each (in real conjoint tests, we can have up to a dozen attributes and up to several dozen levels);
• A multiple linear regression (in real conjoint tests, we use hierarchical Bayesian multinomial logit );
• A fractional factorial design .

The best way to learn more about conjoint analysis is to set up your own study, which you can do when you sign up . You can also read about:

• Alternatives to conjoint (such as MaxDiff and Claims Test )
• Common mistakes and practical tips for setting up conjoint studies
• Check out our webinar on Conjoint Analysis 101

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An introduction to conjoint analysis

Last updated

1 April 2024

Reviewed by

Customers have different preferences that play a role in their purchase decisions. For businesses, meeting these different needs can be challenging. However, conjoint analysis can help make data-driven decisions that optimize products and services, making them more appealing to customers. 

Read on to learn more about the benefits of conjoint analysis and how it can help businesses make informed decisions about product development, pricing, and marketing strategies.

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Save time, highlight crucial insights, and drive strategic decision-making

• What is conjoint analysis?

Conjoint analysis is a survey-based statistical analysis method to understand how customers value products and services and why they make certain choices when buying. 

A product or service comprises multiple conjoined attributes or features, and this is what conjoint analysis focuses on. A conjoint analysis breaks down a product or service into its attributes and tests the different components to reveal customer preferences. 

• Why is it important for researchers?

It helps measure the value the consumer places on each product attribute.

It predicts a combination of features that will have the most value to customers. 

It helps segment customers according to their perceived preferences. This helps with tailoring market campaigns to the right target customers. 

It enables researchers to get customer feedback about an upcoming product. 

• Uses of conjoint analysis

Conjoint analysis is primarily used to make informed decisions relating to:

Buyer decisions

Customer preferences

Market sales

New product pricing

Selection of the best service or product feature

Market campaign validation

• Why use conjoint analysis in surveys?

Conjoint analysis pinpoints what customers value the most, thus revealing their preferences, what they’re prepared to “trade off”, and why.  

• Two types of conjoint analysis 

Two types of conjoint analysis are:

Discrete choice-based conjoint (CBC) analysis

CBC is the most common form of conjoint analysis that asks customers to mimic their buying habits. It asks respondents to choose between a set of product or service concepts. For instance, the choice-based conjoint analysis format presents questions such as "Would you rather?". 

The advantage of discrete choice-based conjoint is that it reflects a realistic scenario of choosing between products rather than directly questioning respondents about each attribute's significance. 

Adaptive conjoint analysis (ACA)

This flexible approach adopts a questionnaire procedure that tailors questions to address personal preferences. The adaptive conjoint analysis targets the respondent's most preferred attribute, thus making the analysis more efficient. 

• When to use it? 

Businesses use conjoint analysis for the following:

Conjoint analysis in pricing

Businesses can use conjoint analysis to ask customers to compare different product features to determine how they value them. It’s an excellent way to learn what features customers are willing to pay for. 

When business owners fully understand what customers value, they can determine the price they’re willing to pay for their products or services. 

Conjoint analysis in sales & marketing

With conjoint analysis, businesses discover customer preferences, allowing them to create marketing campaigns that will target their preferences and increase sales. 

Also, findings of a conjoint analysis could help determine whether there’s enough market for a new product or service.

Conjoint analysis in research & development

With conjoint analysis, product developers can define customer needs and bring the right product or service idea to life. 

In addition, at the beginning of product development , a conjoint analysis will help reveal the concepts that aren’t valued by customers, allowing businesses to eliminate them at the early stages. This saves time and valuable resources and minimizes the risk of a failed product launch. 

• How to do a conjoint analysis

The steps of performing a conjoint analysis are as follows:

Step 1: Define the study problem

Defining the problem establishes the purpose of the experiment. Whether you want to understand your customers better, find a perfect pricing strategy, or predict the market share, problem definition will define the scope of the study. 

In this step, the business owner must consider the target audience and craft specific, meaningful questions. 

Step 2: Break down the product or service into attributes

The next step is to determine the list of attributes of your product or service. Attributes should have varying levels in real life, be clearly defined, and be expected to influence customer preferences and exhibit strong correlations. 

For instance, if you sell cars, the attributes could be engine capacity, trim level, fuel efficiency, color, pricing, warranty, and design. Again, remember to use short descriptions to avoid misunderstandings. 

Step 3: Choose the conjoint analysis methodology

The next step is to organize the questionnaire according to the type of conjoint analysis preferred. 

Choosing CBC is effective when you want respondents to select a preference from a set of choices. ACA is appropriate when you want more accurate information on an individual level. 

Step 4: Deploy the questionnaires to your target respondents 

The questionnaire should have varying features so that the researcher can observe the attributes driving the choice. If the ACA method is used, ask the respondents to rank the attributes based on their needs. 

When the rankings are complete, the researchers get a clear picture of which feature(s) are highly rated by respondents and which aren’t.

Step 5: Data collection and analysis

This step involves collecting data accordingly and using it for decision-making. The rating given by respondents is a raw set of data. The business owner then assigns weights to each category. 

Finally, you can determine the attribute that ranks as the most important, and this will give you information about what customers value the most in your product or service. 

• Five advantages of conjoint analysis

The advantages of using conjoint analysis include the following:

Researchers can determine customer preferences at an individual level.

It reveals the hidden drivers of why customers make certain choices.

It’s a perfect tool for experimenting with attributes such as price before launching a new product or service. 

Conjoint analysis is highly flexible and can be used to develop almost every product or service.

It’s a versatile method that realistically reflects an everyday purchase decision.

• Conjoint analysis examples

The following are two real-world examples of conjoint analysis: 

Example one: A manufacturer seeking to launch a new laptop

When launching a new laptop, manufacturers must know what customers value the most to ascertain what feature draws them to their offerings. Therefore, businesses must conduct a conjoint analysis. The manufacturer will develop a questionnaire that will gather insights from the respondents. 

The attributes that define the laptop are:

The operating system is either Microsoft Windows, Linux, or MacOS. 

The processing speeds

Storage space: is it a 500GB hard drive or 1TB?

Battery life

Screen size

With the help of conjoint analysis, the manufacturer puts a value on each attribute and tailors the product to what’s valued most by a customer. Findings of customer preferences allow the manufacturer to design the "best" laptop technically possible.  

Example two: A restaurant owner seeking to attract a broad customer base 

The restaurant owner may want to differentiate themselves from the competition and attract a wider customer base. They will conduct a conjoint analysis based on what people value the most to understand customer choices. 

People go to restaurants for several reasons, including:

Quality of food

Meal purposes (business, tourist, family, etc.)

Type of food served (seafood, Chinese food, etc.)

The restaurant owner will carry out a conjoint analysis based on the above criteria. The survey response will reveal what customers value the most and allow the restaurant owner to maximize the highly valued feature.

What is an attribute in conjoint analysis?

It’s a product characteristic such as price, size, brand, or color. 

What are attribute levels?

Attribute levels are the values that each characteristic can take. For instance, the attribute shape can have small, medium, large, or extra-large levels. 

How do you identify an attribute?

When defining an attribute, use a language that a customer understands. You can also use images to minimize confusion.

How many people do you need for conjoint analysis?

The sample size for a conjoint analysis depends on the target market. If the target market is relatively small, use a small sample size and vice versa. A general rule of thumb is to use sample sizes that range from 150 to 1,200 respondents. 

What are the real-life applications of conjoint analysis?

You can use conjoint analysis to test the appeal of new products such as soft drinks, footwear, or home appliances. 

How do you calculate market share in conjoint analysis?

You can determine market share by taking a business's sales over a period and dividing it by the industry's total revenue over the same period.

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Conjoint Analysis: Definition, Example, Types, and Model

Have you ever bought a house? As one of the most complex purchase decisions you can make, you must consider many preferences. Everything from the location and price to interest rate and quality of local schools can play a factor in your home-buying decision. You can use conjoint analysis to make data-driven decisions that will help you meet customer needs and develop your organization.

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Less complicated purchases feature a similar process of choosing a good or service that meets your needs. You just may not be aware you’re making those decisions.

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Subconsciously, one person might be more price-sensitive while another is more feature-focused. Understanding which elements consumers consider essential and trivial is the core purpose of conjoint analysis.

Content Index

What is conjoint analysis?

Why is it important for researchers, why use conjoint analysis in surveys, when to use it, how to use conjoint analysis, types of conjoint analysis, conjoint analysis: key terms, when is a good time to run a discrete choice-based conjoint study, advantages of conjoint analysis, conjoint analysis example, conjoint algorithm: how it is works, level-up conjoint analysis insights, conjoint analysis marketing example, how to conduct conjoint analysis using questionpro.

Conjoint analysis is defined as a survey-based advanced market research analysis method that attempts to understand how people make complex choices. We make choices that require trade-offs every day — so often that we may not even realize it. Even simple decisions like choosing a laundry detergent or deciding to book a flight are mental conjoint studies that contain multiple elements that lead us to our choice.

Conjoint analysis is one of the most effective models for extracting consumer preferences during the purchasing process . This data is then turned into a quantitative measurement using statistical analysis. It evaluates products or services in a way no other method can.

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Researchers consider conjoint analysis as the best survey method for determining customer values. It consists of creating, distributing, and analyzing surveys among customers to model their purchasing decision based on response analysis.

QuestionPro can automatically compute and analyze numerical values to explain consumer behavior . Our software analyzes responses to see how much value is placed on price, features, geographic location, and other factors. The software then correlates this data to consumer profiles. A software-driven regression analysis of data obtained from real customers makes an accurate report instead of a hypothesis. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

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Reliable, accurate data gives your business the best chance to produce a product or service that meets all your customer’s needs and wants.

Currently, choice-based conjoint analysis is the most popular form of conjoint. Participants are shown a series of options and asked to select the one they would most likely buy. Other types of conjoint include asking participants to rate or rank products. Choosing a product to buy usually yields more accurate results than ranking systems.

We recommend you take a look at this free resource: Conjoint analysis survey template

The survey question shows each participant several choices of products or features. The answers they give allow our software to work out the underlying values. For example, the program can work out its preferred size and how much it would pay for its favorite brand. Once we have the choice data, there is a range of analytic options. The critical tools for analysis include What-if modeling, forecasting, segmentation, and applying cost-benefit analysis.

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Traditional rating surveys can’t place a value on the different attributes that make up a product. On the other hand, conjoint analysis can sift through respondents’ choices to determine the reasoning for those choices. Analyzing the data gives you the ability to peek into your target audience’s minds and see what they value most in goods or services and acts as a market simulator.

Many businesses shy away from the conjoint analysis because of its seemingly sophisticated design and methodology. But the truth is, you can use this method efficiently, thanks to user-friendly survey software like QuestionPro. Here is a breakdown of conjoint in simple terms, along with a conjoint analysis marketing example.

Over the past 50 years, Conjoint analysis has evolved into a method that market researchers and statisticians implement to predict the kinds of decisions consumers will make about products by using questions in a survey.

The central idea is that consumers evaluate different characteristics of a product and decide which are more relevant to them for any purchase decision. An online conjoint survey’s primary aim is to set distinct values to the alternatives that the buyers may consider when making a purchase decision. Equipped with this knowledge, marketers can target the features of products or services that are highly important and design messages more likely to strike a chord with target buyers.

You can also find best alternatives of Conjoint.ly for your business.

The discrete choice conjoint analysis presents a set of possible decisions to consumers via a survey and asks them to decide which one they would pick. Each concept is composed of a set of attributes (e.g., color, size, price) detailed by a set of levels.

GATHER RESEARCH INSIGHTS

Conjoint models predict respondent preference. For instance, we could have a conjoint study on laptops. The laptop can come in three colors (white, silver, and gold), three screen sizes (11”, 13”, and 15”), and three prices ($200, $400, and $600). This would give 3 x 3 x 3 possible product combinations. In this example, there are three attributes (color, size, and price) with three levels per attribute.

A set of concepts or tasks, based on the defined attributes, are presented to respondents. Respondents make choices as to which product they would purchase in real life. It is important to note that there are a lot of variations of conjoint techniques. QuestionPro’s conjoint analysis software uses choice-based analysis, which most accurately simulates the purchase process of consumers.

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There are two main types of conjoint analysis: Choice-based Conjoint (CBC) Analysis and Adaptive Conjoint Analysis (ACA).

Discrete choice-based conjoint (CBC) analysis:

This type of conjoint study is the most popular because it asks consumers to imitate the real market’s purchasing behavior: which products they would choose, given specific criteria on price and features.

For example, each product or service has a specific set of fictional characters. Some of these characters might be similar to each other or will differ. For instance, you can present your respondents with the following choice:

The devices are almost identical, but device 2 has triple cameras with better configuration, and Device 1 has a higher battery power than Device 2. You would know how vital the trade-off between the number of cameras and battery capacity is by analyzing the responses.

Using the discrete choice model, QuestionPro offers three design types to conduct conjoint analysis:

• Random: This design displays random samples of the possible attributes. For each respondent, the survey software uniquely combines the characteristics. You can run a conjoint concept simulator to know what the choices that the tool will present when you deploy your survey.
• D-Optimal: A flawlessly designed experiment helps researchers estimate parameters without minimum variance and bias. A D-optimal design runs a few tests to investigate or optimize the subject under study. The algorithm helps to create a design that is optimal for the sample size and tasks per respondent.
• Import design: You can also import designs in SPSS format. For example, QuestionPro lets you import fractional factorial orthogonal designs to make use of in surveys.

Adaptive conjoint analysis (ACA):

Researchers use this type of conjoint analysis often in scenarios where the number of attributes/features exceeds what can be done in a choice-based scenario. ACA is great for product design and product segmentation research but not for determining the ideal price.

For example, the adaptive conjoint analysis is a graded-pair comparison task wherein the survey respondents are asked to assess their relative preferences between a set of attributes, and each pair is then evaluated on a predefined point scale.

QuestionPro uses CBC, or Discrete Choice Conjoint Analysis, a great option if the price is one of the most critical factors for you or your customers. The method’s key benefit is that it provides a picture of the market’s willingness to make tradeoffs between various features. The result is an answer to what constitutes an “ideal” product or service.

It is a statistical analysis plan used in market research to gain a better understanding of how people make complex decisions. The following are some key terms of it:

• Attributes (Features): The product features are evaluated by the analysis. Examples of characteristics of Laptops: Brand, Size, Color, and Battery Life.
• Levels:  The specifications of each attribute. Examples of standards for Laptops include Brands: Samsung, Dell, Apple, and Asus.
• Task: The number of times the respondent must make a choice. The example shows the first of the five functions as indicated by “Step 1 of 5.” 5.”
• Concept or Profile : The hypothetical product or offering. This is a set of attributes with different levels that are displayed at each task count. There are usually at least two to choose from.
• Relative importance : “attribute importance,” which depicts which of the various attributes of a product/service is more or less important when making a purchasing decision. Example of Laptop Relative Importance: Brand 35%, Price 30%, Size 15%, Battery Life 15%, and Color 5%.
• Part-Worths/Utility values : Part-Worths, or utility values, is how much weight an attribute level carries with a respondent. The individual factors that lead to a product’s overall value to consumers are part-worths. Example part-worths for Laptops Brands: Samsung – 0.11, Dell 0.10, Apple 0.17, and Asus -0.16.
• Profiles : Discover the ultimate product with the highest utility value. At a glance, QuestionPro lets you compare all the possible combinations of product profiles ranked by utility value to build the product or service that the market wants.
• Market share simulation : One of the most unique and fascinating aspects of conjoint analysis is the conjoint simulator. This gives you the ability to “predict” the consumer’s choice for new products and concepts that may not exist. Measure the gain or loss in market share based on changes to existing products in the given market.
• Brand Premium : How much more will help a customer pay for a Samsung versus an LG television? Assigning price as an attribute and tying that to a brand attribute returns a model for a $ per utility distribution. This is leveraged to compute the actual dollar amount relative to any characteristic. When the analysis is done relative to the brand, so you get to put a price on your brand.
• Price elasticity and demand curve : Price elasticity relates to the aggregate demand for a product and the demand curve’s shape. We calculate it by plotting the demand (frequency count/total response) at different price levels. ADD_THIS_TEXT

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We’re asked this question a lot. So much so that we’ve coined the term Conjoint O’ Clock. If you find yourself needing to get into your customers’ minds to understand why they buy, ask yourself what you hope to get from your insights. It’s time for Conjoint O’Clock if you are trying to:

• Launch a new product or service in the market.
• Repackage existing products or services to the market.
• Understand your customers and what they value in your products.
• Gain actionable insight to increase your brand’s competitive edge .
• Place a price on your brand versus competing brands.
• Revamp your pricing structure.

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There are multiple advantages to using conjoint analysis in your surveys:

• It helps researchers estimate the tradeoffs that consumers make on a psychological level when they evaluate numerous attributes simultaneously.
• Researchers can measure consumer preferences at an individual level.
• It gives researchers insights into real or hidden drivers that may not be too apparent.
• Conjoint analysis can study the consumers and attributes deeper and create a needs-based segmentation.

For example, assume a scenario where a product marketer needs to measure individual product features’ impact on the estimated market share or sales revenue.

In this conjoint study example, we’ll assume the product is a mobile phone. The competitors are Apple, Samsung, and Google. The organization needs to understand how different customers value attributes, such as brand, price, screen size, and screen resolution. Armed with this information, they can create their product range to match consumer preferences.

Conjoint analysis assigns values to these product attributes and levels by creating realistic choices and asking people to evaluate them.

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It enables businesses to mathematically analyze consumer or client behavior and make decisions based on real insights from customer data. This allows them to develop better business strategies that provide a competitive edge. To fulfill customer wishes profitably requires companies to fully understand which aspects of their product and service are most valued.

We use a logic model coupled with a Nelder-Mead Simplex algorithm. It helps to calculate the utility values or part-worths. This algorithm’s benefit allows QuestionPro to offer a cohesive and comprehensive survey experience all within one platform.

We understand that most businesses don’t need the intricate details of our mathematical analysis. However, we want to provide you with the transparency you need to use conjoint survey results. Have confidence in your results by reviewing the algorithm below.

• Let there be R respondents, with individuals r = 1 … R
• Let each respondent see T tasks, with t = 1 … T
• Let each task t have C concepts, with c = 1 … C
• If we have A attributes, a = 1 to A, with each attribute having La levels, l = 1 to La, then the part-worth for a particular attribute/level is w’(a,l). In this exercise, we will be solving this (jagged array) of part worths.
• We can simplify this to a one-dimensional array w(s), where the elements are {w′(1, 1), w′(1, 2)…w′(1, L1), w′(2, 1)…w′(A, LA)} with w having S elements.
• A specific concept x can be shown as a one-dimensional array x(s), where x(s)=1 if the specific attribute is available, and 0 otherwise.
• Let X rtc  represent the specific concept of the c th  concept in the t th  task for the r th respondent. Thus, the experiment design is represented by the four-dimensional matrix X with size RxTxCxS.
• If respondent r chooses concept c in task t then let Y rtc =1; otherwise 0.
• The value Ux of a definite idea is the total of the part-worths for those elements available in the conception, i.e. the scalar product of x and w.

Multinomial logit model

For a simple choice between two concepts, with utilities U1 and U2, the multinomial logit (MNL) model predicts that concept 1 will be chosen.

Modeled Choice Probability

Let the choice probability (using MNL model) of choosing the cth concept in the tth task for the r th respondent be:

Log-Likelihood Measure

Solving for Part-Worths using Maximum Likelihood

We solve for the part-worth vector by finding the vector w that gives the maximum value for LL. Note that we are solving for S variables.

• This is a multi-dimensional, nonlinear continuous maximization research problem , and it is essential to have a standard solver library. We use the Nelder-Mead Simplex Algorithm.
• The Log-Likelihood function should be implemented as a function LL(w, Y, X) and then optimized to find the vector w that gives us a maximum. The responses Y, and the design.

X is specified and constant for a specific development. The starting values for w can be set to the origin 0. The final part-worth values, w, are re-scaled so that the part-worths for any attribute have a mean of zero. This is done by subtracting the mean of the part-worths for all levels of each quality.

Although conjoint analysis requires more involvement in survey design and analysis, the additional planning effort is often worth it. With a few extra steps, you get an authentic look into your most significant customer preferences when choosing a product.

Price, for example, is vital to most folks shopping for a laptop. But how much more is the majority willing to pay for longer battery life for their laptop if it means a heavier and bulkier design? How much less in value is a smaller screen size compared to a slightly larger one? Using conjoint surveys, you’ll discover these details before making a considerable investment in product development.

Conjoint is just a piece of the insights pie. Capture the full story with a cohesive pricing, consumer preference, branding, or go-to-market strategy using other question types and delivery methodologies to stretch the project to its full potential. With QuestionPro, you can build and deliver comprehensive surveys that combine conjoint analysis results with insights from additional questions or custom profiling information included in the survey.

Gather research insights

Click on the Add New Question link and select the Conjoint (Discrete Choice) Option from under Advanced Question Types. This will open the wizard-based conjoint question template to create tasks by entering attributes (features) and levels for each of the features.

For example, an organization produces televisions and they are a competitor of Samsung, LG, or Vizio. The organization needs to understand how different customers value specific attributes such as the size, brand, and price of a television. Armed with this information, they can create their very own product range and offering that meets a market need and generates revenue.

Step 2: Enter the features and levels.

Enter the features and levels. Set up the task counts and concepts per task and assign feature types: Price, Brand, or Other. Using television brands as an example, consider the following:

• Features for televisions: Price, Size, Brand.
• Price:$800, $1,200, $1,500
• Size: 36”, 45”, 52”
• Brand: Sony, LG, Vizio

Step 3: Select Design Type to either of the three design types: Random, D-Optimal, and Import.

Step 4: Add additional setting options, including fixed tasks and prohibited concepts.

Step 5: Preview, review text data, and distribute the survey.

In this example, the survey would look like this:

Where can I view Reports for the conjoint questions?

Step 1:  Go To  Login »  Surveys »  Analytics »  Choice Modelling »  Conjoint Analysis

Step 2: Here, you can view the online reports.

Step 3: You can download the data in Excel/CSV or HTML format.

The QuestionPro conjoint analysis offering includes the following tools:

• Conjoint Task Creation Wizard: Wizard-based interface to create Conjoint Tasks based on merely entering features(attributes), like price and levels, like $100 or $200, for each feature.
• Conjoint Design Parameters: Tweak your design by choosing the number of tasks, the number of profiles per task, and the “Not-Applicable” option.
• Utility Calculation: Automatically calculates utilities.
• Relative Importance: Automatically calculates the relative importance of attributes (based on utilities).
• Cross/Segmentation and Filtering: Filter the data based on criteria and then run Relative Importance calculations.

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Conjoint analysis is an effective market research technique that helps businesses better understand their customer’s preferences and make educated decisions about product creation, pricing, and marketing strategies.

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The conjoint analysis provides significant insights into how customers assess different aspects when making purchase decisions by breaking down complex purchasing decisions into smaller components and examining them systematically. 

There are several types of conjoint analysis models accessible, each with its own set of advantages and disadvantages. Choosing the best model is determined by the study objectives and the specific characteristics of the market under consideration.

Conjoint analysis is a valuable tool for any company wanting to obtain a better knowledge of its customers and keep ahead of the competition in today’s ever-changing market. If you are thinking about conducting conjoint analysis, QuestionPro is there for you. 

QuestionPro provides a comprehensive set of features and tools to assist businesses in conducting conjoint analysis efficiently and effectively, making it a valuable tool for market research professionals. Contact QuestionPro right away!

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The Plain-English Guide to Conjoint Analysis

Published: February 23, 2022

Sometimes, commercials really get me.

T-Mobile 's Super Bowl commercial this year is a prime example — "What's for Dinner?" demonstrates the infuriating process of choosing what to do for dinner for a young couple, and it's gold .

The reason T-Mobile's ad was so relatable is because of their market research. They looked at what their target audiences wanted — including their thought processes, what informs their decisions, and the trade-offs they're willing to make for their products.

To accomplish all of these important factors in one go, many companies use conjoint analysis.

What is Conjoint Analysis?

Conjoint analysis is a market research tactic that attempts to understand how people make decisions. A common approach, the conjoint analysis combines realistic hypothetical situations to measure buying decisions and consumer preferences.

Think about buying a new phone. Attributes you might consider are color, size, and model. The reason phone companies include these specs in their marketing is due to research such as conjoint analysis.

Would consumers purchase this product or service if brought to market? That's the question conjoint analysis strives to answer. It's a quantitative measure in marketing research, meaning it measures numbers rather than open-ended questions. Questions on the phone company survey would include price points, color preference, and camera quality.

Surveys intended for conjoint analysis are formatted to reflect the buyer's journey.

For instance, notice in this example for televisions, the specs are the options and the consumer picks what best reflects their lifestyle:

This direct method of giving consumers multiple profiles to then analyze is how conjoint analysis got its name. These answers are helpful when determining how to market a new product.

If answers on the phone company survey proved that their target audience of adults ages 18-25 wanted a green phone from $400-600 and a camera with portrait mode, advertisements can cater directly to that.

The conjoint analysis shows what consumers are willing to give up in order to get what they need. For instance, some might be willing to pay a little more money for a larger model of a phone if their preference is larger text.

Types of Conjoint Analysis

Choice-based conjoint (CBC) and Adaptive Conjoint Analysis (ACA) are the two main types of conjoint analysis.

Choice-based is the most common form because it asks consumers to mimic their buying habits. ACA is helpful for product design, offering more questions about specs of a product.

Choice-based conjoint analysis questions are usually presented in a "Would you rather?" format. For example, "Would you rather take a ride-share service to a location 10 minutes away for $13 or walk 30 minutes for free?" The marketer for the ride-share service could use answers from this question to think of the upsides to show off in different campaigns.

ACA leans towards a Likert-scale format (most likely to least likely) for its attribute-based questions. Respondents can base their preference on specs by showing how likely they are to buy a product with slight differences — for example, similar cars with different doors and manufacturers.

How To Do A Conjoint Analysis

To create a conjoint analysis, you'll first need to define a list of attributes about your product. Attributes are usually four to five items that describe your product or service. Consider color, size, price, and market-specific attributes, such as lenses if you're selling cameras.

Additionally, try to keep in mind your ideal respondents. Who do you want to answer your survey? A group of adult men? A group of working mothers? Identify your respondent base and ask specific questions catered to that target market.

The next step is to organize your questionnaire depending on the type of conjoint analysis you want to conduct. For instance, to run an adaptive conjoint analysis, you will present questions with a Likert-scale.

You can use a conjoint analysis tool to create and modify your survey. Then, you can distribute your questionnaire through multiple channels, including email, SMS, and social media.

For more ways to introduce product marketing into your company, check out our ultimate guide here .

Examples of Conjoint Analysis

Sawtooth Software offers a great example of conjoint analysis for a phone company:

The analysis puts three different phone services next to each other. The horizontal column of the model identifies which service is offering a certain program, described by the vertical values. The bottom row shows a percent value of consumers' preferences.

QuestionPro offers this fun, interactive conjoint analysis template about retirement home options. The survey gives you a scenario and asks your course of action. For instance, it asks if you would sign a rental agreement for retirement home housing immediately, and considers specs like rent, meals, size, etc.

Conjoint analysis isn't limited to existing products. They're also very helpful for figuring out if a brand-new product is worth developing. For instance, if surveys show that audiences would be into the idea of an app that chooses clothes for consumers, that could be a new venture for clothing companies in the future.

Looking to create a conjoint analysis of your own? Check out our top four conjoint analysis tools below.

Conjoint Analysis Tools

1. qualtrics.

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Qualtrics is an easy-to-use survey tool that offers comprehensive product insights. You can create, modify, distribute, and analyze a conjoint analysis in one place. All it takes is four steps — define your attributes, build and modify your questions in the survey editor, distribute the survey, and analyze the results. 

What We Like: Qualtrics goes beyond product insights — this powerful software also captures customer, brand, and employee experience insights.

Pro Tip: Leverage email to invite respondents to take your survey. With Qualtrics, you can embed a survey question directly in your email survey invite. 

2. Cojoint.ly

Conjoint.ly offers a complete toolbox for product and pricing research — including a Product Description test, an A/B test, and a Price Sensitivity test. You can also source your own respondents for your survey or buy quality-assured respondents from Conjoint.ly.

What We Like: Users can simply choose a tool that best fits their research question. These tools are organized under four main categories: pricing research, features and claims, range optimization, and concept testing.

Pro Tip: If you want to "try before you buy," you can use Conjoint.ly's Quick Feedback tool. For a small price, you get around 50 respondents to provide feedback within a 6-hour window.

3. 1000minds

1000minds offers an adaptive conjoint analysis tool. Meaning, each time a choice is made, it adapts by formulating a new question to ask based on all previous choices. This makes the survey feel more like a conversation.

What We Like: We're impressed by the scalability of 1000minds. The tool allows you to include as many participants as you like, potentially in the thousands.

Pro Tip: You can use their conjoint analysis templates or build your own model from scratch. 

4. Q Research Software

Q is analysis software that is specifically designed by market researchers. Its conjoint analysis tool is ideal for choice-based analyses. Users can create experimental designs, analyze the data, and generate reports. 

What We Like: Q cuts through the grunt work with automation — including cleaning and formatting data, updating surveys, and producing reports.

Pro Tip: With just a few clicks, you can export any reports or visualizations from Q to PowerPoint and Excel.

A conjoint analysis requires a solid survey design and analysis, but the extra effort is often worth it. By going the extra mile, you can access insights into your audience's preferences and buying decisions — which is invaluable when determining how to market a new product or service.

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Conjoint Analysis Definition, Types, and Examples

Conjoint analysis is a market research technique used to understand how consumers value different features of a product or service. It involves presenting respondents with a series of hypothetical scenarios and asking them to choose their preferred option.

Table of contents

What is Conjoint Analysis?

What are the types of conjoint analysis.

• Why is Conjoint Analysis important for Researchers?

Benefits of Conjoint Analysis

Drawbacks of conjoint analysis, conjoint analysis examples, tips for using conjoint analysis.

Have you ever wondered how companies determine the perfect combination of product features that will appeal to their customers? One popular technique used by market researchers is called “conjoint analysis.” This method involves presenting survey respondents with different product configurations and asking them to rate or rank their preferences.

By analyzing the data, researchers can identify which product attributes are most important to consumers and how they interact with one another. In this article, we’ll dive into the definition, types, and examples of conjoint analysis to help you better understand this valuable research tool.

Conjoint analysis is a statistical technique used in surveys to understand how people make decisions and evaluate products or services based on their attributes. It involves presenting participants with a series of hypothetical scenarios that vary in the attributes of the product or service being evaluated. By analyzing the choices participants make in these scenarios, researchers can determine the relative importance of different attributes and how they affect overall preference.

Conjoint analysis can provide valuable insights into consumer behavior and help businesses make informed decisions about product development, pricing, and marketing. It is commonly used in market research, product design, and customer satisfaction studies.

• Hot-Button Conjoint Analysis This type focuses on the emotional response of respondents to features and aspects of products or services. It can provide valuable insights into the correlation between emotional responses and purchase decisions.
• Pairwise Comparisons Choice-based analysis is a survey-based method used in market research, new product design, government policy-making, and the social sciences to understand people’s preferences and shape products and policies accordingly. It is based on the 1000minds PAPRIKA technique , which uses questions based on choosing between pairs of alternatives to determine people’s utilities (weights).
• Grid Analysis Grid analysis is a type of market research technique that helps to evaluate the attractiveness of different product or service features. It can help companies determine which features are most important and make sure they include them in their products. Grid analysis can also be useful in helping to identify which features consumers are willing to pay a premium for and which ones they aren’t as interested in. This can be helpful in developing pricing strategies and product design.
• Rating Scale Analysis Rating scale analysis of conjoint data is a type of analysis used to assess consumer preferences and make decisions about product features and marketing strategy. It is different from other forms of conjoint analysis, such as choice-based conjoint analysis, as it does not directly link to behavioral theory. It is limited in the number of attributes that can be included in the study, but it provides an effective way to understand consumer preferences and make decisions about product features and marketing strategy.
• Tree Analysis Tree analysis is a type of conjoint analysis often used in market research to understand the customer’s preferences for different product attributes. This type is different from other analyses in that it uses a hierarchical structure to organize and rank customer preferences. For example, a tree analysis could differentiate between a brand preference, such as “HP” vs. “Dell” versus the actual product attributes, such as processor type, hard disk size and amount of memory.
• MaxDiff Conjoint Analysis MaxDiff analysis is a type of market research methodology used to determine the relative values of combinations of features by asking customers to rate them from best to worst. It is similar to other forms of conjoint analysis, such as Choice-Based Conjoint (CBC) Analysis, Adaptive Conjoint Analysis (ACA), and Full-Profile Analysis, but differs in that it presents a smaller set of product profiles for evaluation. This makes the task easier for respondents, and MaxDiff can also be used with other research techniques to provide more detailed insights into customer preferences.
• Multi-Way Analysis A multi-way analysis is used to measure the reactions to a range of product attributes by creating a matrix of choices. Unlike traditional analysis which only presents a single attribute or feature to the respondent at a time, multi-way analysis presents multiple attributes or features to the respondent for consideration in a single-choice task. This allows the researcher to understand how different combinations of attributes affect the respondent’s preference. Multi-way analysis can also be used in combination with other forms of conjoint analysis such as choice-based conjoint (CBC), adaptive conjoint analysis (ACA), full-profile conjoint analysis, MaxDiff conjoint analysis, and hierarchical Bayesian Analysis (HB).
• Choice Modeling Choice modeling is a type of analysis that looks at the choices that customers make when they are presented with several options. It is used to understand the trade-offs that consumers make when evaluating different attributes of a product, and can be used to uncover hidden drivers that may not be apparent to respondents. It also mimics realistic choices or shopping tasks and can be used to develop needs-based segmentation in some cases.

Why is Conjoint Analysis Important for Researchers?

Conjoint analysis is one of the most important tools for researchers as it helps them to gain insights into a consumer’s preferences and decision-making processes on an individual level. It allows for a deeper study of the consumers and attributes involved to create a needs-based segmentation, providing user-based affirmation of what is most valued in the product or service. This helps researchers to understand the trade-offs that consumers make when they evaluate multiple attributes simultaneously, giving them insight into the real and hidden drivers that may not be readily apparent.

Furthermore, researchers are able to measure consumer preferences and analyze data to gain statistically relevant insights representative of a larger group. As a result, conjoint analysis has become the gold standard for preference research and is used by many businesses in different industries across the globe.

By using surveys, businesses can measure the value that different features have for consumers. This information can be used to create products and services that better meet the needs of customers. By understanding what customers value most, businesses can create offerings that increase satisfaction.

Conjoint analysis is a technique that can be used to find the best combination of product features by surveying customers. First, determine the features you would like to examine, and select the target customers to survey. Then, reach out to customers with a survey that presents them with different combinations of features and asks them to rank them based on their preference. After the surveys are returned, analyze the results to determine the optimal feature set for your needs.

This analysis can be used to estimate the market share of new products by gathering data from customers on their preferences for different product alternatives and attributes. This data is then used to create a choice model which estimates the likelihood of each product being chosen by potential customers.

Conjoint analysis is a tool that can help businesses identify which product features are most valuable to customers. By conducting a conjoint analysis survey, businesses can determine which features are the most important to their customers and develop a marketing strategy that is most successful.

Conjoint analysis can be used to evaluate the effectiveness of advertising campaigns by determining what consumers are willing to pay for certain features and attributes. By analyzing the data collected from a conjoint study, marketers can gain a better understanding of what consumers are willing to buy, which allows them to refine their advertising strategies.

Conjoints require careful consideration of multiple attributes and levels, which can lead to a complex design. As the number of attributes and levels increases, so does the complexity of the design, making it difficult to manage and analyze.

Conjoints often involve asking respondents to evaluate a large number of product profiles, which can lead to respondent fatigue. This can result in lower response rates and lower-quality data as respondents may not be fully engaged in the survey.

The results of conjoint analysis are specific to the attributes and levels included in the design. This means that the results may not be generalizable to other products or markets, limiting the usefulness of the analysis.

Conjoints assume that respondents make decisions based on a rational evaluation of the attributes and levels presented to them. However, in reality, decision-making is often more complex, and emotional and psychological factors can also play a role.

Here are four examples of how conjoint analysis can be used in real-world scenarios:

• Hotel Room Preferences – A hotel chain wanted to know which room features were most important to their guests, such as the size of the room, the view, and the amenities. Using this analysis, they presented survey respondents with different room configurations and asked them to rate their preferences. The analysis revealed that a spacious room and a good view were the most important factors for guests.
• Fast Food Menu Optimization – A fast food chain was looking to optimize their menu by determining which items and prices would be most appealing to customers. Using conjoint analysis, they presented survey respondents with different menu options and asked them to rank them. The analysis revealed which items were the most popular and at what price points they were most appealing.
• Car Purchase Decisions – An automotive manufacturer wanted to understand which car features were most important to consumers when making a purchase decision. Using this analysis, they presented survey respondents with different car configurations and asked them to rate their preferences. The analysis revealed that safety features, fuel efficiency, and performance were the most important factors for consumers.
• Smartphone Preferences – A smartphone manufacturer was planning to launch a new device and wanted to understand which features would be most appealing to consumers. They presented survey respondents with different phone configurations and asked them to rank them by their preference. The analysis revealed that the most important factors for consumers were screen size, battery life, and camera quality. With this information, the manufacturer was able to optimize their new phone’s features and pricing strategy to better meet customer preferences.
• Know the purpose of the analysis and the questions you are trying to answer.
• Identify the factors that are important to customers and the attributes of your product or service that you want to measure.
• Test and refine the design of the questions to ensure they accurately measure the preferences of customers.
• Create scenarios that best reflect what customers would experience in the real world.
• Analyze the data collected and interpret the results to get the most out of your conjoint analysis.
• Leverage the results to create models that help you make better, more informed decisions.
• Consider partnering with a professional data analysis firm for additional insight.

In conclusion, conjoint analysis is a powerful tool for understanding consumer behavior and preferences. It provides a systematic way to evaluate and compare different attributes of products or services and their impact on overall preference. There are several types of conjoint analysis, including full-profile, adaptive, and choice-based, each with their own strengths and weaknesses.

Examples of applications include new product development, pricing research, and customer satisfaction studies. By using conjoint analysis, businesses can gain insights into what factors drive consumer decision-making, and use that knowledge to make informed decisions about product development, pricing, and marketing strategies.

FAQ on Conjoint Analysis

What is conjoint analysis and how does it work.

Conjoint analysis is a market research technique used to determine how consumers value different features of a product or service. It works by presenting participants with a series of hypothetical product or service profiles that vary in terms of their attributes (such as price, quality, and design), and asking them to choose their preferred option from each set.

What are the advantages of using conjoint analysis in market research?

Conjoint analysis can provide valuable insights into how consumers make decisions and what factors influence their choices. It can also help businesses understand how to price their products or services, design new products or services, and target specific consumer segments.

How do you design a conjoint analysis study?

To design a conjoint analysis study, you need to first identify the attributes that are most relevant to your product or service. You then need to create a set of product or service profiles that vary in terms of these attributes, using a statistical technique called fractional factorial design. Finally, you need to recruit participants and present them with the profiles, asking them to choose their preferred option from each set.

What are some limitations of conjoint analysis?

Conjoint analysis relies on participants' ability to accurately evaluate and compare different product or service profiles. If the profiles are too complex or if participants are not familiar with the attributes being tested, the results may not be reliable. Additionally, conjoint analysis assumes that participants make decisions based solely on the attributes presented, which may not be true in real-world situations.

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Choice-Based Conjoint Analysis Guide [Example Questions and Case Study]

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">In this article, we take a look at the benefits of choice-based conjoint (CBC), how and when to conduct a CBC study, what CBC questions look like, and an example of a CBC project.

Table of Contents: 

• What is choice-based conjoint analysis?

Benefits of a choice-based conjoint study 

How to execute a choice-based conjoint analysis .

• When to use choice-based conjoint analysis for your business   
• Examples of choice-based conjoint analysis questions  
• Example of choice-based conjoint analysis study

How quantilope can help with your next choice-based conjoint analysis 

What is choice-based conjoint analysis? 

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">Choice-based conjoint analysis ( dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC ) , also known as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579086">discrete dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579041">choice dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579086" data-dropdown-placement-param="top" data-term-id="281579086"> modeling , is an advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579036">market dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579070">research dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579070" data-dropdown-placement-param="top" data-term-id="281579070"> method that identifies consumers’ preferences when considering a product or service. This is done by asking research dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-offs between competing products, each of which has a variety of attributes. Asking consumers to choose their preferred product reveals the importance of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579058">different attributes in determining consumers’ dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579072">willingness to pay . dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579054">Product attributes might include brand, design features, price, or style; dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels (within each attribute) might be Ford and Toyota, built-in nav system, heated seats, sporty or family style, etc.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is the most commonly used type of conjoint analysis. It differs from other conjoint approaches in that it presents consumers with full product profiles (rather than just asking them to rate attributes separately, as in two-attribute dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-off analysis) and it allows for the inclusion of price as a determining attribute (which is not an ideal use case for another type of conjoint - dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579068">adaptive conjoint analysis; this type of conjoint changes as each person answers the survey questions to consider their individual preferences ).

Back to Table of Contents

Authenticity

Because dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents are presented with profiles that detail the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579058">different attributes contained within each product, this method mimics a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579064">real-world purchase scenario. Buying a product can be a complex process, with subconscious decisions made along the way, so asking dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to make product dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-offs  reveals which attributes and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels truly drive the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579062">purchase decision .

Attribute valuation

Traditional surveys ask dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to rank or rate attributes and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels , which can give an indication of how important different features are when consumers make purchases. However, the problem with considering attributes in isolation is that this lacks the contextual information required to assess how likely a purchase will be. For example, bread buyers might say that they rate whole grains, added vitamins, and bread softness highly, but it can be difficult for them to say which of those attributes is more important than others.

Forcing consumers to make dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579039">trade-offs reveals the relative dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579050"> importance and value of each attribute. Some product profiles in a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC might not even be chosen at all, revealing attributes that are of little or no importance to consumers (and therefore, not worth the investment). Each attribute’s value metric is known as a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579043">part-worth utility score, and is calculated for each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute level in a study. This is a great springboard for a needs-based dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579034">segmentation that defines what different consumer groups are looking for in a product. Knowing each attribute’s valuation is helpful for designing products and ensuring the whole product package is attractive to consumers. After determining the top attributes, you can use a conjoint analysis to ensure that when these different parts are combined, the product is still overall appealing.

The effect of price

Determining the optimal price level for a product is incredibly important, but can be difficult to measure in a research study as dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents will almost always say that price is important to them and that they want the lowest price possible. Further, price isn’t a fixed attribute with a limited dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579069">number of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579038" data-dropdown-placement-param="top" data-term-id="281579038"> levels - it can always be increased or (to a degree) decreased.

With choice-based conjoint, brands can test out different price levels in their product profiles to identify the overall price range that consumers will consider buying their product. This is one of the best ways to identify a ‘fair’ and justified price to charge for your products. Beyond determining price level, conjoint can also project revenue modeling to find a sweet spot between a price index and actual revenue.

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">Respondent enjoyment

Because a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579067">conjoint survey feels like a real-life scenario, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents enjoy the ability to choose between different product profiles rather than simply answering questions about separate attributes or giving individual rankings/scores. Back to Table of Contents

There are a number of factors to consider when designing and conducting a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC survey. At the design stage, brands need to decide on the following:

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579056">Sample size:   This needs to be big enough to provide meaningful data on consumer preferences. The number of r dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">espondents needed will depend on the complexity of the design, but a general guideline is to have a few hundred dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents for each product profile you’re measuring.

Choice type: Choose how dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents will evaluate each set of product profiles (i.e. combinations of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels ). You might want to force dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents to make a single choice from the sets of products shown or provide them with a ‘none of the above’ option.

Number of profiles per set:  Decide how many product profiles should be shown per set. Too many profiles can become tedious for dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents , while too few profiles won’t provide enough comparative data.

Number of sets per dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent:  Similar to the above, decide how many overall ‘sets’ of products each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent will evaluate so that dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents aren’t overwhelmed but still provide enough data for analysis.

Attributes: These are the features of each product or service you’re researching. These might include price, size, color, brand, and style. Aim for no more than six attributes to avoid overloading dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents .

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">Attribute levels: The variations within each attribute - such as large, medium, and small; blue, red, white, and yellow. Again, aim for no more than six levels to keep dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents engaged.

Once the above factors have been established, a brand will launch their choice-based dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579067">conjoint survey amongst their target audience. Randomized dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579059">choice sets of product profiles are shown to each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent , and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents choose their favorite product from each set. At the analysis stage, each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute level ’s dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579043">part-worth utility is calculated, as well as its dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579050">relative importance to other dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels in the study.

quantilope offers a fully automated approach to dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC , from survey design to final analysis. Its survey templates and pre-programmed dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC method ensure all relevant information is included in a conjoint study. quantilope’s dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579077">CBC dropdown#toggle" data-dropdown-menu-id-param="menu_term_281579077" data-dropdown-placement-param="top" data-term-id="281579077"> analysis output includes a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579042">market simulator that projects how different product profiles would be received by consumers in the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579064">real world and identifies propositions with the highest consumer appeal and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579072">willingness to pay . Back to Table of Contents

When to use dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis for your business   

dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">Choice-based conjoint analysis is used across a broad range of business areas, from consumer packaged goods (CPG) to services and healthcare. Wherever there is the possibility of different product or service propositions, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is an excellent way to determine which profile would be most appealing and profitable.

If your business wants to explore any of the following, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is a great dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodology to leverage:

Projected dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579046">market share

If you have an idea for a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579040">new product or a revamp of an existing one, it pays to know whether it will sell well once launched. A common use of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is to determine which feature combination will claim the largest dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579046">market share .

Nailing down dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579045">product features

If you’re at the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579047">product development stage and have an idea of features for your product but don’t know which will be most important to consumers, dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC will tell you which ones, and with which combinations, to include for maximum consumer appeal.

The right price for a product

A crucial question for any business is how to price its offer. With dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC , each product profile can include price as one of the attributes and the analysis will reveal the perceived value of product benefits (i.e. what consumers are willing to pay for the features a product has). It will also give a good idea of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579071">price sensitivity - i.e. how a product’s demand is affected by price - and how dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579046">market share will affect revenue at different price levels. Back to Table of Contents

Examples of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis questions  

A dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis questionnaire can look different depending on the product or service being tested, or on the survey platform used, but the general principle is always the same.

If you’re conducting a survey on smartphones, your dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579059">choice sets presented to each dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent could look something like this:

The smartphone profile that a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondent opts for will give an insight into which features they place the most importance on. For example, they might sacrifice a better camera for a longer battery life, or choose a larger screen despite lower storage.

As another example, a conjoint question for hand soap could include the following attributes:

Will dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579032">respondents go for an antibacterial hand wash, whatever the price? Is size a key factor because they have a large family? Are all these profiles too expensive for hand soap, and a respondent would choose 'none of these options'?

As a third example, a restaurant conducting a conjoint questionnaire might include the following attributes to see which menu items are most appealing:

If the restaurant were planning a new menu, the conjoint data would help narrow in on which menu items are most appealing, what the atmosphere should be like, and how they should price their meals. Back to Table of Contents

Example of dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis study  

quantilope’s automated dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis is a popular dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodology for many platform users and clients. One such client is PAX , a leading global cannabis brand that wanted to gather consumer insights around a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579040">new product offer in a growing market. dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579082">Product design and innovation were essential to PAX’s growth, so using a dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC to explore product formats and benefits was key.

Using quantilope’s dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC , PAX was able to present a range of product possibilities to consumers and, by means of automated analysis, understand dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579057">attribute importance and benefit dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579066">configurations that would appeal to the most consumers.

“Two weeks after we signed on with quantilope I got a direct request from our CEO to run a Conjoint analysis. I would not have been able to do it without quantilope; my other option would have been to find a specialist and lose time requesting and reviewing proposals.” -Kristen Archibald, Sr. Consuemr Insights Manager at PAX

For more on this successful dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579044">conjoint study , access the full case study here . Back to Table of Contents

How quantilope can help with your next dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis  

quantilope’s expertise in AI-driven dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodologies (including dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis ) provides brands with the confidence needed to design a successful product or service offer.

Although dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC is a sophisticated and complex dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579070">research method , quantilope makes the process seamless and straightforward. Simply select the dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579033">CBC from quantilope’s list of pre-programmed advanced dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579052">methodologies , design your product profiles with dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579085">various attributes and dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579038">attribute levels , ‘configure’ the remaining setup in one click, and set your survey live.

Review your conjoint analysis data through a variety of charts that show things like an optimal price point, acceptable price range, average dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579043">part-worths , individual dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579057">attribute importance , and more. Then, merge all your findings into one interactive, shareable dashboard with automated significance testing.

For more information on how quantilope can help your business test dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579040">new product profiles and features through automated dropdown#toggle" data-dropdown-placement-param="top" data-term-id="281579035">choice-based conjoint analysis , get in touch with us below!

Get in touch to learn more about choice-based conjoint!

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An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research

• Perspectives
• Published: 23 October 2014
• Volume 2 , pages 19–40, ( 2015 )

Cite this article

• James Agarwal 1 ,
• Wayne S. DeSarbo 2 ,
• Naresh K. Malhotra 3 , 5 &
• Vithala R. Rao 4  

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This review article provides reflections on the state of the art of research in conjoint analysis—where we came from, where we are, and some directions as to where we might go. We review key articles, mostly contemporary published since 2000, but some classic, drawn from the major marketing as well as various interdisciplinary academic journals to highlight important areas related to conjoint analysis research and identify more recent developments in this area. We develop an organizing framework that attempts to integrate various threads of research in conjoint methods and models. Our goal is to (a) emphasize the major developments in recent years, (b) evaluate these developments, and (c) to identify several potential directions for future research.

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1 Introduction

Conjoint analysis is one of the most celebrated research tools in marketing and consumer research. This methodology which enables understanding consumer preferences Footnote 1 has been applied to help solve a wide variety of marketing problems including estimating product demand, designing a new product line and calibrating price sensitivity/elasticity. The method involves presenting respondent customers with a carefully designed set of hypothetical product profiles (defined by the specified levels of the relevant attributes), and collecting their preferences in the form of ratings, rankings, or choices for those profiles.

Since the introduction of conjoint analysis in marketing research over four decades ago, a remarkable variety of new models and parameter estimation procedures have been developed. Some of these include the move from nonmetric to metric orientation and orthogonal experimental designs in the 1970s, developments in choice-based and hybrid conjoint including adaptive conjoint model in the 1980s, the growing popularity of hierarchical Bayesian and latent class models in the 1990s, and the adaptability of conjoint models to online choice tasks, incentivized contexts, group dynamics, and social influences in the past decade. Several earlier review articles in marketing and consumer academic research have documented the evolution of conjoint analysis. Footnote 2 This manuscript provides an organizing framework for this vast literature and reviews key articles, critically discusses several advanced issues and developments, and identifies directions for future research. Cognizant of the fact that conjoint analysis has matured, this review is selective in the choice of articles, some classic but mostly contemporary focusing on the developments during the period post-2000; that have made or have the potential for having maximal impact in the field. Hopefully, this interdisciplinary review will encourage conjoint scholars to evolve beyond existing conjoint models and explore new problems and applications of consumer preference measurement, develop new forms of data collection, devise new estimation procedures, and tap into the dynamic nature of this methodology.

2 An Organizing Framework for Conjoint Analysis

The developments in conjoint research have naturally drawn from a variety of disciplines (notably choice behavior and statistical theory). The conceptual framework shown in Fig.  1 attempts to integrate various threads of research across five major categories: ( A ) Behavioral and Theoretical Underpinnings , ( B ) Researcher Issues for Research Design , ( C ) Respondent Issues for Data Collection , ( D ) Researcher Issues for Data Analysis , and ( E ) Managerial Issues Concerning Implementation . This framework considers all three relevant stakeholders: the researcher, the respondent, and the manager.

A framework for organizing contemporary research in conjoint analysis

3 (A) Behavioral and Theoretical Underpinnings

3.1 a1. behavioral processes in judgment, preference, and choice.

The developments in the judgment and decision-making research offers great potential for conjoint analysis to better understand the behavioral processes in judgment, preference, and choice. We now know how, and increasingly, why characteristics of task and choice option guide attention and how internal memory and external information search affect choice in path-dependent ways. Footnote 3 Recent research illustrates that preferences are typically constructed rather than stored and retrieved [ 111 ].

Judgments and choices typically engage multiple psychological processes, from attention-guided encoding and evaluation to retrieval of task-relevant information from memory or external sources, including prediction, response, and post-decision evaluation and updating. Attention is more important in decisions from descriptions (e.g., the full-profile approach of conjoint analysis) whereas memory and learning is more relevant in decisions from experience through trial and error sampling of choice options [ 77 ]. Footnote 4 On the other hand, in decisions from experience, recent outcomes are given more weight and rare events get underweighted. In a similar vein, the insight that evaluation is relative from prospect theory continues to gain support [ 184 ]. Since neurons encode changes in stimulation, rather than absolute levels, absolute judgments are much more difficult than relative judgments. Relative evaluation includes other observed or counterfactual outcomes from the same or different choice alternatives, as well as expectations.

Also relevant to conjoint analysis are the recent extensions of decision field theory (DFT) and models of value judgment in multiattribute choice [ 94 ]. In these models, attributes of choice alternatives are repeatedly randomly sampled and each additional acquisition of information increases or decreases the valuation of an alternative in a choice set, ending when the first option reaches a certain threshold. DFT as a multilayer connectionist network has also been applied to explain context effects such as similarity, attraction, and compromise effects [ 143 ]. For instance, conjoint models that capture compromise effect result in better prediction and fit compared to traditional value maximization models [ 102 ].

While stimulus sampling models typically assume path-independence, choice models are often biased toward early-emerging favorites resulting in reference-dependent subsequent evaluations [ 97 ] and distortion of the value of options, i.e., decision by distortion effect [ 16 , 158 ]. Also, studies of anchoring suggest that the priming of memory accessibility (and hence preference) can be changed by asking a prior question and remains strong in the presence of incentives, experience, and market feedback [ 9 ]. Not only are there short-term changes, but long-term effects on memory have been shown; for example, measuring the long-term effects of purchase intentions on memory and subsequent purchases [ 28 ].

The recently developed query theory (QT) [ 95 ] on preference construction is a process model of valuation describing how the order of retrievals from memory plays a role in judging the value of objects, emphasizing output interference. Weber et al. [ 185 ] show that the queries about reasons supporting immediate versus delayed consumption are issued in reverse order thus making the endowment effect disappear. Similar to Luce’s choice axiom, the support theory (ST) [ 176 ] is a model of inference about the probability of an event that uses the relative weight of what we know and can generate about the event (its support) and compares it to what we know and can generate about all other possible events [ 184 ]. Since competing hypotheses are often generated by associative memory processes from long-term memory, irrelevant alternative hypotheses may well be generated and occupy limited-capacity working memory [ 52 ]. This has implications for conjoint research as consumers with greater working memory capacity can include more alternative hypotheses (i.e., explicit disjunctions) and thus greater discrimination and lower judged probability of the focal brand being chosen.

Recently, the dual process models of System 1 and System 2 processes proposed by Kahneman [ 97 ] have gained popularity. Psychological models have distinguished between a rapid, automatic and effortless, associative, intuitive process (System 1) and a slower, rule-governed, analytic, deliberate, and effortful process (System 2). The extent and the process in which the two systems interact [ 57 ] is a topic of debate. Both cognitive and affective mechanisms have been demonstrated to give rise to the discounting of future events such as in delayed versus immediate consumption [ 185 , 178 ]. These theories have implications for conjoint data collection for technology products or durable goods.

3.1.1 Suggested Directions for Future Research

While time-discounted utility models are useful in inter-temporal choice, there is also a need to incorporate various behavioral effects in conjoint models. Conjoint modelers can extend and augment inter-temporal utility specifications by using temporal inflation parameters representing differences in “internal noise” used by behavioral researchers. An example is the recent critique by Hutchinson et al. [ 86 ] of the theoretical assumptions made by Salisbury and Feinberg’s [ 150 ] stochastic modeling of experimental data, where they empirically tested alternate models of choice and judgment with respect to assumptions relating to “internal noise” and “uncertainty about anticipated utility” as well as the stochastic versus deterministic nature of the scale parameters.

Conjoint analysts can develop utility models that extend prospect theory and neuron-encoded relative judgments to better understand how consumers select reference points and how multiple reference points might be used in relative evaluation [ 184 ]. For instance, individual heterogeneity can be captured through a distribution of reference points rather than a single reference point such as reference price [ 50 ].

Decision-makers may pay equal attention to all possible outcomes than is warranted by their probabilities and linger at extreme outcomes to assess best and worst choices in choice-based conjoint studies. Cumulative prospect theory that explains the evaluation of outcome probabilities relative to its position in the configuration of outcomes [ 175 ] can provide a useful avenue for research for this problem.

The power of affect, feelings, and emotions in consumer judgment, preference, and choice is now well established [ 118 ]. Future conjoint research should incorporate the mechanisms of the dual process model, i.e. System 1 and System 2 models [ 97 ]. Also, decision affect theory provides a framework that incorporates emotional reactions to counterfactual outcome comparisons such as regret or loss aversion [ 34 ]. In a risky choice situation, the fit with self-regulatory orientation can also transfer as affective information into the choice task which could be modeled [ 78 ].

3.2 A2. Compensatory Versus Noncompensatory Processes

Much of the conjoint research assumes that the utility function for a choice alternative is additive and linear in parameters. Footnote 5 The implied decision rules are compensatory. Generally speaking, linear compensatory choice models do not address simplifying choice heuristics such as truncation and level focus that can result in an abrupt change in choice probability. Yet, noncompensatory simple heuristics are often more or at least equally accurate in predicting new data compared to linear models that are criticized for over-fitting the data [ 67 , 89 , 103 ]. While the linear utility model has been the mainstay in conjoint research, Bayesian methods, including data augmentation, can easily accommodate nonlinear models and can deal with irregularities in the likelihood surface [ 6 ]. Recently, Kohli and Jedidi [ 103 ] and Yee et al. [ 190 ] propose dynamic programming methods (using greedy algorithm) to estimate lexicographic preference structures.

Noncompensatory processes are particularly relevant in the context of consideration sets, an issue typically ignored by the traditional conjoint research (e.g., [ 67 , 91 ]). Many advocate a noncompensatory rule for consideration and a compensatory model at the choice stage (consider-then-choose rule), albeit some critics question the existence and parsimony of a formal consideration set (see Horowitz and Louviere [ 81 ] who find the same utility function at the two-stage versus one-stage only). For instance, in a study estimating consideration and choice probabilities simultaneously, Jedidi et al. [ 91 ] find that both segment-level and individual Tobit models perform better than the traditional conjoint model which ignores both consideration as well as error component in preference. Similarly, Gilbride and Allenby [ 67 ] estimate a two-stage model using hierarchical Bayes methods, augmenting the latent consideration sets within their MCMC approach. Recently, Hauser et al. [ 76 ] propose two machine-learning algorithms to estimate cognitively simple generalized disjunctions-of-conjunctions (DOC) decision rules, and Liu and Arora [ 114 ] develop a method to construct efficient designs for a two-stage, consider-then-choose model. Footnote 6 Stuttgen et al. [ 164 ] propose a continuation of the line of research started by Gilbride and Allenby [ 67 ] and Jedidi and Kohli [ 89 ] that does not rely on compensatory trade-offs at all. These finding are consistent with economic theories of consideration set wherein consumers balance search costs with option value of utility maximization to achieve cognitive simplicity.

3.2.1 Suggested Directions for Future Research

It seems that combining lexicographic and compensatory processes in a two-stage model using the greedoid algorithm in the first stage is a promising research route to follow as it enhances the ecological rationality of preference models (see [ 67 , 103 ], and [ 89 ]).

Several interesting behavioral processes such as the formation and dynamics of the consideration set still need to be understood. Given the technological advances (i.e., eye-tracking technology) in dealing with noncompensatory processes and satisficing rules, it behooves conjoint researchers to adapt such methods in the future (see [ 164 , 173 ], and [ 157 ]).

Knowledge about cue diagnosticity Footnote 7 and take-the-best (TTB) strategy performs really well when the distribution of cue validities is highly skewed. Several other heuristics have also performed well including the models that integrate TTB and full information [ 80 , 109 ]. We encourage conjoint researchers to incorporate cue diagnosticity in estimating noncompensatory models.

While the recognition heuristic (RH) for inference in cases in which only one of two provided comparison alternatives is recognized as a useful tool, the debate is whether recognition is always used as a first stage in inference or whether recognition is simply one cue in inference that can be integrated (see [ 132 , 142 ]) without any special status. For future research, RH can be potentially applied in conjoint-choice contexts that are characterized by rapid, automatic, and effortless processes (i.e., System 1 process [ 97 ]) typical in low-involvement routine products.

3.3 A3. Integrating Behavioral Learning and Context Effects

Conjoint analysis has made some significant gains in incorporating behavioral theory into preference measurement. Recently, Bradlow et al. [ 20 ] investigated how subjects impute missing attribute levels when they evaluate partial conjoint profiles using a Bayesian “pattern-matching” learning model. Respondents impute values for missing attributes based on several factors including their priors over the set of attribute levels, a given attribute’s previously shown values, the previously shown values of other attributes, and the covariation among attributes. Alba and Cooke [ 3 ] critique that not all attributes are spontaneously inferred and even when inference is natural, symmetry may be violated such that the probability of imputing cause (e.g., quality) from effect (e.g., price) may deviate from the probability of imputing effect from cause. When information is intentionally retrieved, the weighting function may reflect uncertainty about the accuracy of the profiles or the ability to retrieve them.

There is substantial research in conjoint analysis to demonstrate context effects. Conjoint models in marketing research have assumed stable preference structures in that preferences at the time of measurement are the same as at the time of trial or purchase. However, context effects produce instability when the context at measurement does not match the context at decision time [ 15 , 110 ]. DeSarbo et al. [ 45 ] introduced a Bayesian dynamic linear model (DLM)-based methodology that permits the detection and modeling of the dynamic evolution of individual preferences in conjoint analysis that occur during the task due to learning, exposure to additional information, fatigue, cognitive storage limitations, etc. (see [ 113 ]). Also, see Rutz and Sonnier [ 149 ] for Bayesian modeling (i.e., DLM method) of dynamic attribute evolution due to market structural changes for more details.

Kivetz et al. [ 102 ] find that incorporating the “compromise effect” leads to superior predictions and fit compared with the traditional value maximization model. Recently, Levav et al. [ 110 ] demonstrated using experimental studies that normatively equivalent decision contexts can yield different decisions, which challenges the assumption that people maximize utility and possess a complete preference ordering. This type of research attempts to bridge consumer psychology with marketing science. Other related work involving dynamic preference structures include Netzer et al. [ 129 ], Evgeniou et al. [ 59 ], Bradlow and Park [ 19 ], Fong et al.[ 64 ], Ruan et al. [ 148 ], Rooderkerk et al. [ 145 ], De Jong et al. [ 38 ], and Elrod et al. [ 56 ].

3.3.1 Suggested Directions for Future Research

There is clearly a need for more rigorous work to incorporate behavioral effects in preference measurement. While this may create a conflict between isomorphic goal of fit and paramorphic goal of predictive validity [ 130 ], a greater dialogue and collaboration between the two research camps is essential for improved quality of conjoint research.

Future research in this conjoint arena should examine other documented behavioral effects such as asymmetric dominance, asymmetric advantage, enhancement, and detraction effects (see [ 2 ]).

Since preference formation is a dynamic process dependent on learning and context effects, future researchers should attempt to further develop and use flexible models and dynamic random-effects models such as those used by Liechty et al. [ 113 ], and Bradlow et al. [ 20 ]. Many of the flexible models developed to capture dynamics in repeated choice (e.g., [ 107 ]) could be adapted to conjoint preference measurement.

It would be worthwhile to investigate how choice probabilities change in choice-based conjoint and choice simulators when context effects and consumer expertise are built directly into the model as these may affect the likelihood and form of missing attribute inference [ 3 ].

3.4 A4. Group Dynamics and Social Interactions

A vast majority of choice models assume that a consumers’ latent utility is a function of brand attributes, and not the preferences of referent others. However, some scholars have examined the influence of referent others in a dyadic and network context. For instance, Arora and Allenby [ 10 ] develop a Bayesian model to estimate attribute-specific influence of spouses in a decision-making context and discuss how and whom marketers can target communication messages effectively. Using a Bayesian autoregressive mixture model, Yang and Allenby [ 189 ] demonstrate that preference interdependence due to geographically defined networks is more important than demographic networks in explaining behavior. Ding and Eliashberg [ 47 ] proposed a new model that explicitly considers dyadic decision-making in ethical drug prescriptions in the context of physician and patients. The issue of reducing hypothetical biases (e.g., socially desirable responses [ 48 ]) in group dynamics through innovative methodology, such as incentive-aligned conjoint studies, is critical.

Some exciting work has started using conjoint models in the domain of group dynamics and social interactions [ 129 , 29 , 68 , 88 , 127 , 159 ]. Footnote 8 With the availability of “sentiment analysis” tools, firms are now able to extend beyond ratings data and capture a torrent of online textual communications from a variety of social media including blogs, chat rooms, new sites, YouTube, and Twitter. Footnote 9 Recently, Sonnier et al. [ 159 ] using the web crawler technology and automated classification of sentiments were able to demonstrate that positive and negative comments increased the dynamic stock while negative comments decreased it and that such effects are masked when the comment volume is aggregated across valence. Based on the theory of social contagion [ 88 ], Narayan et al. [ 127 ] study the behavioral mechanisms underlying peer influence affecting choice decisions and find that consumers update their inherent attribute preferences in a Bayesian manner by utilizing the relative uncertainty of their attribute preference and that of their peers and use peer choices as an additional attribute. This particular study is significant as the authors mitigate problems of endogeneity, correlated unobservable variables, and simultaneity by setting up a preinfluence and post-influence conjoint experimental design. Most recently, Kim et al. [ 101 ] introduced a holistic preference and concept measurement model called PIE for conjoint analysis which is a new incentive-aligned data collection method which allows a consumer to obtain individualized shopping advice through other people.

3.4.1 Suggested Directions for Future Research

In the promising domain of group dynamics and social interactions for technology-based products, one important research question would be to ask what role can internal versus external motivations of online information disseminators play in changing the posterior beliefs and preference structure of consumers [ 69 ]? For example, very little is known as to what motivates opinion leaders and early adopters to not just possess but share information with others.

There is a vast potential for conjoint models to draw from consumer research on reference group formation and social influences on buyer choice behavior such as internalization, identification, and compliance [ 141 , 156 ]. In this area, barter conjoint offers a promising potential to model the effects of information diffusion among subjects and how endowment and loss-aversion effects [ 101 , 22 , 49 ] induce individuals to behave differently than conventional choice behavior.

We issue a call for scholars to explore further developments in conjoint models that capture online recommender systems and social interactions given the rising importance of social media [ 32 ]. Existing algorithms using Classification and Regression Trees, Bayesian Tree Regression, and Stepwise Componential Regression can be further combined to develop an optimal sequence of questions to predict online visitor’s preference [ 37 ]. Additional research into problems involving multiple decision makers with multiple utility functions (e.g., in business-to-business applications) would prove valuable.

4 (B) Researcher Issues for Research Design

Conjoint researchers have long dealt with the problem of large number of attributes and levels with the help of experimental designs. The specific choice will depend on a variety of factors including objectives of the research, cost, time, statistical sophistication, and the need to develop individual-level estimates, etc. We focus on the research designs related to conjoint approaches that are more popular: choice-based conjoint analysis, menu-based experimental choice, and maximum difference best/worst conjoint method. We also briefly discuss some recent developments in experimental design and handling of large number of attributes.

4.1 B1. Choice-Based Conjoint Analysis

Choice-based conjoint (CBC) analysis describes a class of hybrid techniques that are among the most widely adopted market research methods for conjoint analysis (see [ 137 ]). Footnote 10 The early choice-based hybrid models used stage-wise regression, compositional models to fit self-explicated data, and the decompositional model at the segment level. However, hybrid models were later extended to allow for parameter estimation at the individual level using self-explicated data for within-attribute part-worth estimation, and using the full-profile approach for improving estimates of attribute importance.

Recent developments have allowed for estimation at the individual level through Bayesian estimation [ 71 , 167 ], even though a respondent provides only a small amount of information within CBC. In the same vein, it is not clear whether segments obtained from CBC are similar to those found from post hoc clustering of part-worths [ 25 ]. One aspect of choice-based models, particularly with the development of multinomial logit estimation procedures, is the property of independence of irrelevant alternatives (IIA) that forces all cross-elasticities to be equal. However, researchers have developed ways to deal with the IIA assumption by employing mixed-logit or random-parameters logit that allows for flexible variance-covariance structures. Building on recent work by Louviere and Meyer [ 116 ] and Louviere et al. [ 117 ], Fiebig et al. [ 61 ] argue that much of the heterogeneity in attribute weights is accounted for by a pure scale effect (i.e., holding attribute coefficients fixed, the scale of the error term is greater) leading to scale heterogeneity MNL model. Also noteworthy is the recent development in detecting and statistical handling of attribute nonattendance in which respondents focus on a subset of attributes only in choice-based conjoint. Scarpa and colleagues use two different panel mixed-logit models to account for response pattern of repeated exclusion that influence model estimation (see [ 154 ], [ 155 ], and [ 24 ]).

4.1.1 Suggested Directions for Future Research

Several marketing scholars (see [ 130 ], [ 70 ], and [ 83 ]) identified the importance of advanced research into the direct modeling of behavioral effects on decision-making and choice (e.g., in choice-based conjoint analysis). The research issues include understanding of such behavioral phenomena as self-control, context effects, inattention, or reference dependence. The embedding of meta-attributes such as expectations, goals, motivations, reference groups, and social networks might also prove gainful in conjoint analysis.

Another potential area of study is the modeling of individual-level structural heterogeneity. More specifically, are there some combination of attribute levels that create a change in the structure of the utility function utilized by a specific consumer? While conjoint scholars have explored compensatory vs. noncompensatory models for a given choice-based conjoint task, work involving potential regime shifts during the task by consumer would prove insightful (see [ 63 ]).

4.2 B2. Menu-Based Experimental Choice

In menu-based conjoint analysis, customers are asked to pick several features from a menu of features or products that are individually priced. If the utility of each feature is above a certain threshold, it is chosen and the utilities of all the chosen features are maximized simultaneously resulting in multiple chosen alternatives [ 112 ]. The responses therefore entail a binary vector of choices for each respondent for each of the menu scenarios in the experiment. This is quite akin to choosing a bundle of items [ 31 ] from a larger set or designing a product using a product configurator as buying, for instance, a Dell laptop. Configurators represent a promising form of conjoint data collection in which the respondent self-designs the best product configuration [ 112 ]. Recently, Levav et al. [ 110 ] argue that in a mass customization decision (such as using a configurator), consumers can often lose their self-control in assessing utility correctly in repeated choice situations due to bounded rationality and the depletion effects of their mental resources [ 181 ]. Dellaert and Stremersch [ 40 ] borrowing from choice theory and task complexity theory also demonstrated that consumers’ product utility had a positive effect on mass customization utility while task complexity had a negative effect, albeit lower for experts.

In addition to the many menu choices that it generates, menu-based choice represents a modeling challenge that is distinct from the traditional single-choice analysis of data from choice-based conjoint experiments—e.g., using multinomial logit models or multinomial probit models. The Bayesian modeling approach in this context, entailing a constrained random-effects multinomial probit model [ 112 ], incorporates constraints in menu choices (e.g., firm-level design or production constraints) as well as heterogeneity in customers’ price sensitivities and preferences for the variety of customized options a firm can offer. In this multiple choice modeling scenario, researchers can assess the intrinsic worth of each feature, their price sensitivities, and model correlations among them for each individual. Web-based menus would allow firms to offer mass-customized services with every potential customer visiting their web site.

4.2.1 Suggested Directions for Future Research

Given the ability of menu-based conjoint to provide individual-level information and the growing reality of web-based mass customization, we encourage researchers to further study customer heterogeneity in demand and new channels of information exchange to maximize customer value.

Conjoint scholars can add to our understanding of mass-customized choice processes by explicating individual traits, task factors, and decision strategies that influence customization complexity. To further refine the model, future conjoint scholars can incorporate a more general distance model that can explicitly account for the relative differences between attribute levels, unlike the 0–1 pattern-matching model (see [ 20 ]). This can be accomplished by combining conjoint analysis and MDS to impute missing attribute levels. When the number of attributes is large, mapping between attributes and some higher-order dimensions can be developed (i.e., conjoint utility functions) a la MDS methods. Methods of reverse mapping can yield part-worth values for the original attributes. But, this approach needs to be developed and validated.

One other promising line of research here would be to study whether consumers enjoy mass customizing a product or service, and at what levels of complexity will they make suboptimal choices. It is possible that consumers also overspend their mental capacity early in the configuration sequence triggering a tendency to accept the default alternative in subsequent decisions, even when such decisions involve few options that would require less capacity to evaluate. A related issue in need of further investigation is minimizing the dysfunctional consequences of information overload in conjoint studies.

4.3 B3. Maximum Difference Scaling—Best/Worst Conjoint

Based on a multinomial extension of Thurstone’s model for paired comparisons, Finn and Louviere [ 62 ] developed a univariate scaling model (MaxDiff) that can be utilized to measure brand-by-attribute positions, develop univariate scales from multiple measures, etc. Swait et al. [ 166 ] describe how to generalize or extend MaxDiff to conjoint applications which they call Best/Worst conjoint analysis or B/W. In the B/W method respondents choose the two attribute levels which are, respectively, “best” and “worst” for each product profile. With such data, the method enables the estimation of separate attribute effects for each attribute independently of its part-worths. This is an important advantage over the traditional additive conjoint and choice models that do not allow for such separation [ 166 ]. B/W experiments have also been found to contain less respondent error than choice-based conjoint models containing the same attributes and levels [ 166 ]. Other advantages include allowing for ties in evaluations unlike ranking tasks and a more discriminating way to measure attribute importance than either rating scales or the method of paired comparisons. Also, it has greater predictive validity as an importance measurement than either ratings scales or the method of paired comparisons. B/W measurements are scale-free and thus ideal for comparison across different cultural groups that use scales quite differently [ 33 ] without any need to make prior assumptions regarding the scaling of evaluation and choice. Consequently, maximum difference scaling has been used extensively in Best/Worst Conjoint Analysis. However, some limitations include evaluating both positive and negative attributes, effects of having only best or worst features versus best and worst, collinearity, and sequence effects, among others. For example, MaxDiff results are shown to be less accurate at the “best” end but augmentation (e.g., Q Sort) improves MaxDiff results on “best” items [ 53 ].

4.3.1 Suggested Directions for Future Research

Best/Worst allows for ties in evaluations and for skewed preference functions, unlike ranking tasks. Whether or not B/W and choice-based conjoint produce equivalent part-worth utilities, after adjusting for the difference in respondent error, is currently unknown as the results have been mixed [ 166 ]. More research is needed to further validate the B/W method.

More recently, Marley and Louviere [ 121 ] have developed several different probabilistic B/W choice models: the Consistent Random Utility B/W choice model, the MaxDiff model, the biased MaxDiff model, and the concordant B/W choice model (see also [ 122 ]). However, questions remain about whether the B/W method can be used in accordance with the random utility theory. A related question is whether the judgments respondents make in a B/W task could be used as though they had made in an alternative-based choice, ranking, or rating using compensatory rules.

4.4 B4. Developments in Experimental Design

Rating-based methods in marketing conjoint studies have frequently utilized resolution III designs (or orthogonal arrays), which assume that some main effects are confounded with some two-level interactions. In general, orthogonal designs for linear models are efficient as measured by A-, D-, and G-efficiency computed from eigenvalues of the \( {\left(X\hbox{'}X\right)}^{-1} \) matrix (recently, Toubia and Hauser [ 169 ] proposed the criterion of managerial efficiency, M-efficiency, as well). Kuhfeld [ 106 ] showed that the OPTEX procedure produces more efficient designs; however, it fails to achieve the perfect level balance or the proportionality criteria of orthogonal arrays. In the case of choice-based conjoint methods, Huber and Zwerina [ 85 ] show that achieving utility balance increases the efficiency. Building on their work, Sandor and Wedel [ 151 ] develop Bayesian-based efficient designs (through relabeling, swapping, and cycling) that minimize the standard errors with higher predictive validity. Subsequently, Sandor and Wedel [ 152 ] develop efficient designs that are optimal for mixed-logit models by evaluating the dimension-scaled determinant of the information matrix of the mixed multinomial logit model. Because choice-based conjoint model is nonlinear, both minimal overlap and utility balance in the choice set are desirable. Rose et al. [ 146 ] extend the Sandor and Wedel study to construct statistical S-efficiency that optimizes Bayesian designs for a given sample size based on parameter values, random-parameters logit mixing distributions, and model specifications [ 146 , 99 ]. However, the trade-off is that choice task difficulty typically is accompanied with greater measurement response error, and thus a lower response R-efficiency.

Despite several developments, some limitations remained, such as the need to obtain repeated observations from each respondent, the use of aggregate-customization design that was optimal for the average respondent only, and the challenge of computing ordinary Fisher’s information matrix. This was later partly addressed by Sandor and Wedel [ 153 ] who used a small set of different designs for different consumers to capture respondent heterogeneity. Recently, Yu et al. [ 191 ], using the generalized Fisher information matrix, proposed an individually adapted sequential Bayesian approach to generate a conjoint-choice design that is tailor-made for each respondent. The method is superior both in estimation of individual-level part-worths (and population-level estimates) and choice prediction compared to benchmarks such as aggregate-customization and orthogonal design approaches. Further, this method is less sensitive to low-response accuracy as compared to the polyhedral method proposed by Toubia et al. [ 171 ] and their subsequent adapted method [ 172 ]. New developments are also emerging in the area of choice set designs with forced choice experiments. For example, Burgess and Street [ 21 ] developed procedures to construct near-optimal designs to estimate main effects and two-level interactions with a smaller numbers of choice sets and they derive the relevant mathematical theory for such designs; see [ 21 , 163 , 161 , 162 ] for detailed descriptions.

4.4.1 Suggested Directions for Future Research

Newer methods of adaptive questions based on active machine-based learning method are proving very successful over market-based, random, and orthogonal-design questions when consumers use noncompensatory heuristics; see Abernethy et al. [ 1 ] and Dzyabura and Hauser [ 54 ]. We encourage more research along this direction.

The trade-off between S- and R-efficiency is an interesting issue to resolve going forward. While greater S-efficiency yields smaller variance, increasing R-efficiency by reducing task complexity with attribute overlap reduces S-efficiency. While inconclusive, more research needs to be done whether efficient experimental designs contribute more to the precision of choice model estimates in light of task complexity (see [ 99 ]).

4.5 B5. Handling a Large Number of Attributes

A comprehensive review of various methods for dealing with large number of attributes is available in Rao et al. [ 140 ]. Several scholars are currently working on the issue of handling large numbers of attributes [ 35 , 128 ]. For instance, Dahan [ 35 ] simplified the conjoint task (using Conjoint Adaptive Ranking Database System) by asking respondents to choose only among a very limited number of sets that are perfectly mapped to specific utility functions proposed in advance by the researcher. Park et al. [ 134 ] proposed a new incentive-aligned web-based upgrading method for eliciting attribute preferences in complex products (e.g., cameras); this method enables participants to upgrade one attribute at any level from a large number of attributes allowing for dynamic customization of the product. Their empirical application shows that the upgrading method is comparable to the benchmarked self-explicated approach, takes less time, and has a higher external validity.

Recently, Netzer and Srinivasan [ 128 ] proposed a web-based adaptive self-explicated (ASE) approach to solve the self-explicated constant sum question problem when the number of product attributes becomes large. The ASE method breaks down the attribute importance question into a ranking of the attributes followed by a sequence of constant sum paired comparison questions for two attributes at a time thus replacing the importance measurement stage of the traditional self-explication model. The attribute importance is estimated by using a log-linear regression model (with OLS estimation) which gives the benefit of estimating standard errors as well. The ASE method significantly and substantially improved predictive validity as compared to the self-explication model, adaptive conjoint analysis, and the fast polyhedral method.

As with the large number of attributes problem, researchers should also consider the number-of-levels effect. As the number of intervening attribute levels increase, the derived importance of an attribute also increases. Prior studies have linked this phenomenon to data collection methodology, measurement scale of the dependent variable, and parameter estimation procedures [ 179 ], but results are somewhat inconclusive. More recently, De Wilde et al. [ 39 ] explain this phenomenon by focusing on selective attention, and argue that attentional contrast directs attention away from redundant attribute levels and toward novel attributes in sequential evaluation procedure (e.g., in traditional full-profile conjoint analysis and choice-based conjoint).

4.5.1 Suggested Directions for Future Research

The search for methods for coping with large number of attributes has been identified as one of the key areas for future research [ 18 ]. An approach that holds promise is to have subsamples of respondents provide data on a subset of attributes with some linkages among the sets as in bridging conjoint analysis. Hierarchical Bayesian methods can then be applied to such data to estimate part-worths at the individual level. We encourage conjoint scholars to further advance this line of research.

Given scant research, there is a need for studies, using simulations as well as empirical data, to compare the relative efficacy of the different methods in handling large number of attributes. Future research should assess how measurement technique, attribute representation, and experimental design will influence the relative novelty of an attributes’ levels at the time of measurement. Further, conjoint scholars should engage in developing algorithms that are sensitive to level balance across attributes, especially for unbalanced designs.

5 (C) Respondent Issues for Data Collection

Over the years, conjoint research has focused either on preference ratings (or rankings) of a number (between a dozen to thirty) of carefully designed product profiles (a la ratings-based methods) or on stated choice for each of several choice sets of product profiles, including a no choice option. When the number of attributes becomes large (i.e., over six), methods such as adaptive methods or partial profile methods have been employed. These approaches have come to a stable situation. Not many research issues seem to exist in this arena. Rather, we will focus on newer methods such as using incentive alignment and willingness to pay, barter conjoint and conjoint poker, meta-attributes and complexity of stimuli, and the role of no-choice option given their recent development and future research potential.

5.1 C1. Incentive Compatibility and Willingness to Pay

Ding et al. [ 48 ] found strong evidence in favor of incentive-aligned choice conjoint in out-of-sample predictions and a more realistic preference structure that exhibited higher price sensitivity, lower risk-seeking behavior, and lower susceptibility to socially desirable behaviors. This development has cast doubt on the assumption that purchase intent and choice are related in stated preference data. However, a real challenge is for researchers to implement incentive alignment in really new or complex products when it is not cost effective to offer real product to each participant or to generate all product variations.

Dzyabura and Hauser [ 54 ] addressed the cost issue by implementing an active machine-learning algorithm which approximates the posterior with a distributional variation and uses belief propagation to update the posterior distribution. The questions are selected sequentially to minimize the expected posterior entropy by anticipating the potential responses, i.e., to consider or not to consider. Their study confirms that consumers use cognitively simple heuristics with relatively fewer aspects and that the adaptive questions search the space of decision rules efficiently. Ding [ 46 ] addressed the issue of “all product variations” by developing a truth-telling mechanism by incentivizing conjoint participants which becomes the Bayesian Nash Equilibrium. The BDM procedure ensures that it is in the best interest of a participant to have his or her inferred willingness to pay equal to his or her true willingness to pay.

Conjoint methods are typically used for measuring the willingness to pay (WTP). WTP becomes more relevant in the context of incentive-aligned upgrading of attributes [ 134 ]. Wathieu and Bertini [ 183 ] used categorization theory to argue that a moderately incongruent price differential is more likely to induce deliberation when a new benefit is added or augmented beyond consumer expectations. Dong et al. [ 51 ] proposed a Rank Order mechanism that predicts preferences for a list of reward products, instead of an individual’s monetary value for one product, and gives or sells the top-rated one to the respondent. They recommend the WTP mechanism when there is only one real product and price can be estimated from preference measurement task; and the Rank Order method when two or more real versions of the product are available regardless of whether or not WTP can be estimated.

The contingent valuation method, typically used to determine the WTP for a nonmarket good, is subject to exaggeration bias which stems from factors such as new product enthusiasm, an attempt to influence the decision to market the product, or a tendency to be less sensitive to total costs [ 93 , 180 ]. One approach is to calibrate the responses into quasi-real ones based on self-assessed certainty; however, the latter measure can also be fraught with survey bias. The second approach has been transforming the hypothetical WTP into real WTP assuming a functional relationship. Park and MacLachlan [ 133 ] propose an exaggeration bias-corrected contingent valuation method in which the individual compares the real WTP with an independent randomly drawn spurious WTP and then takes the larger one as his or her hypothetical WTP. The real WTP is only assumed to be related randomly with the hypothetical WTP rather than have a functional relation.

Voelckner [ 180 ] found significant and substantial differences between WTP reported by subjects when payment of the stated price is real or hypothetical. The author compared hypothetical and real WTPs across and within four methods of measuring WTP (i.e., first-price sealed bid auction, the Vickrey auction, contingent valuation, and conjoint analysis). There was evidence of overbidding bias as a result of perceived competitive pressure resulting in higher WTPs for auctions compared to methods based on stated preference data. Recently, Miller et al. [ 125 ] compared the performance of four approaches to measure WTP based on direct versus indirect assessment and hypothetical versus actual WTP with real purchase data. Their findings show that respondents are more price-sensitive in incentive-aligned settings than in nonincentive-aligned settings and in real purchase setting, and are better suited to assess WTP for product prototypes. Overall, recent developments in this domain have been very significant with a promising future outlook.

5.1.1 Suggested Directions for Future Research

While the Rank Order method of incentive compatibility has proven very valuable in motivating truth responses, there is still a need to sort out a host of issues such as desired versus undesired products to be included in the list, the incentive value of products, and whether the incentive list should be revealed before or after the conjoint task.

Given that WTP is a latent construct, research for its validation should be undertaken employing SEM methodology; for instance employing an induced value experiment that provides incentive-compatible estimates of WTP may come closest to mapping the true representation of WTP as a latent construct [ 134 , 46 ].

Giving respondents time to think (TTT) in a contingent valuation study by designing a quasi-experimental study that mimics realistic decision contexts may alter the WTP. How does information and time affect responses to contingent valuation conjoint studies? This is an excellent opportunity for bridging research in consumer psychology, marketing science, and environmental and information economics [ 27 ].

While WTP research typically focuses on estimating marginal rates of substitution (i.e., WTP for marginal changes in product attributes), there is potential scope for data enrichment by combining stated preference and revealed preference; the former providing robust estimates for substitutability and the latter providing robust estimates for predicting uptake behavior (see [ 126 ] for associated statistical estimation methodologies).

5.2 C2. Barter Conjoint and Conjoint Poker

Barter conjoint approach collects substantially larger amount of pairwise data (offers submitted or not and the responses to offers received) without demanding much additional effort, as well as potentially improving the quality of data by allowing information diffusion among participants during preference measurement. Ding et al. [ 49 ] using two studies and two holdout tasks found that the barter conjoint significantly outperformed both incentive-aligned and hypothetical CBC in out-of-sample prediction. Toubia et al. [ 173 ] recently developed and tested an incentive-compatible conjoint poker game and compared it with incentive-compatible choice-based conjoint using a series of experiments. Their findings indicate that conjoint poker induces respondents to consider more of the profile-related information presented to them (i.e., greater involvement and motivation) as compared with choice-based conjoint. Similar to the incentive-compatible mechanisms that add motivation to respondents [ 48 ], conjoint poker motivates respondents toward truth telling.

5.2.1 Suggested Directions for Future Research

Future research in these relatively new approaches could be developed in a number of different directions. For example, applications of barter and poker methods could also be tested for products that are less desirable, allowing for increases or decreases in group assignments, and/or allowing for multiple trades.

There is the restriction that the barter requires synchronized implementation and simultaneous bartering which makes online conversion somewhat cumbersome. Future barter research should examine newer procedures that do not tend to promote possible endowment and loss-aversion effects. Finally, the current estimation method does not model any dynamic effects in preference formation despite the various stages of the barter.

5.3 C3. Meta-Attributes and Complexity of Stimuli

Conjoint researchers need to recognize that consumers often think of products in terms of “meta-attributes” including needs, motivations, and goals which may correspond to bundles of attributes [ 130 ]. Research in judgment and decision-making has incorporated the role of multiple goals and how situational and task factors including goal-framing effects [ 123 ] activate and chronically elevate their accessibility which in turn determine decision rules—e.g., deontological goal of “what is right”, consequentialist goal of “what has the best outcomes”, versus affective goal of “what feels right” [ 13 ]. Also, consequences associated with an attribute that is central in consumers’ hierarchy of goals are likely to generate primary appraisals [ 118 ]. These meta-level preferences can impact decision-making and they tend to be more stable than context-specific preferences. We know that customers think of products in terms of meta-attributes and hierarchy of goals, and that attributes that serve a consequentialist goal are more likely to be accessible and appraised [ 118 , 130 ].

In the context of complex stimuli, i.e., really new products, the role of uncertainty and consumer learning mechanisms through mental simulation and analogies is critical. Some advances have been made in this domain (see [ 73 , 79 ]), but the results are still preliminary. In a related vein, there is also evidence of inconsistency between the importance of attributes as estimated in value-elicitation surveys (i.e., stated preferences) and those implied by actual choices (i.e., revealed preferences). Horsky et al. [ 82 ] empirically demonstrate that attributes may be differentially weighted in stated preference versus actual choice as a function of their tangibility, such that tangible and concrete attributes are weighted more heavily in choice since consumers are under pressure to justify their decisions. Going forward, we offer the following issues for future research.

5.3.1 Suggested Directions for Future Research

One big challenge is to conceptually map the relationship between physical (i.e., concrete) attributes and meta-attributes in a way that can be translated into product design specifications. Some concrete attributes may lose their meaning when interpreted at a higher level of abstraction and generality, thus undermining the validity of responses [ 31 ].

The other challenge is methodological, although some work in this domain has started using factor analysis, text mining, and tree-based methods (e.g., Classification and Regression Trees, Bayesian Tree Regression) as valuable tools in this respect [ 37 , 66 ]. While factor analysis is feasible, it lacks the ability to create maps between physical attributes and meta-attributes. We encourage continued research in this area.

5.4 C4. The Role of the No Choice Option

Parker and Schrift [ 135 ] argued that the mere addition of a no-choice option to a set changes the consumers’ judgment criteria from comparative judgment (i.e., attribute-based processing) to an evaluative judgment (i.e., alternative-based processing). Through a series of studies, the authors demonstrate that the mere addition of a no-choice option (i.e., rejectable choice set) leads to alternative-based recall (encoding and retrieval) and information processing, greater weights being given to attributes that are enriched (more meaningful when evaluated alone) and those that meet consumers’ minimum needs, and ultimately a change in preference. The perceived difference between alternatives will be increasingly smaller the further the attributes are from the consumers’ threshold. Consistent with the literature on context effects [ 15 ], this study confirms that consumers shift their preference structure between a forced choice context and a rejectable choice context and ultimately choice shares. It is conceivable that every decision a consumer makes has a no-choice option and conjoint scholars should design studies that add the no-choice option when it is feasible and salient for consumers. Further, Botti et al. [ 17 ] suggest that mostly all choices consumers make are restricted or constrained in some manner.

5.4.1 Suggested Directions for Future Research

Potential distortions as arising due to variations in choice sets need to be examined by-product/service class, type of experimental design, method of administration, etc. to fully understand the impact of the specific methodology selected to perform conjoint analysis.

A number of interesting subareas on the impact of choices made when a “no choice” option is included need further investigation. These include the frequency in which the “no choice” option is selected, the impact of “no choice” selection on estimated importance, and whether the choices are sequenced or staged (i.e., first consider, then decide to choose) [ 114 ].

6 (D) Researcher Issues for Data Analysis

Major developments in the estimation procedures relevant for the conjoint researcher include Hierarchical Bayesian, Latent Class, and Polyhedral Estimation approaches. Further, opportunities exist in integrating multiple sources of data to obtain robust conjoint results.

6.1 D1. The Hierarchical Bayesian (HB) Approach

The HB method of estimation is helpful in tackling the challenge in conjoint analysis to estimate accurate part-worths at the individual level without imposing excessive response burden on the respondents. HB methods have been known to improve on finite mixture-based individual-level estimates which tend to be more stable than estimates that are based on individual data [ 4 ]. Following earlier pioneering work, Footnote 11 Allenby et al. [ 5 ] utilized the Bayesian method and the Gibbs sampler to extend research by incorporating prior ordinal constraints on conjoint part-worths and found better internal cross-validation on the data. Often, there is a logical or practical ordering of the attribute levels that exists in the real world.

Subsequently, Srinivasan and Park [ 160 ] proposed a new method to optimize the full-profile design for a large number of attributes and provided a heuristic procedure to weigh together the part-worth estimates of the self-stated and full-profile data on a smaller number of core attributes. By differentiating between core and noncore attributes, they predicted preference for a new stimulus by using the optimal weight and conjoint part-worths for the core attributes and the self-explicated part-worths for the noncore attributes. Andrews et al. [ 8 ] showed that HB models performed well even when the part-worths came from a mixture of distributions and were robust to violations of the underlying assumptions. In almost all instances, the Bayesian method has been found to be comparable or even superior to the traditional methods both in part-worth estimation and predictive validity. Sandor and Wedel [ 153 ] demonstrated that heterogeneous designs which take into account Bayesian design principles of prior uncertainty and respondent heterogeneity showed substantial gains in efficiency compared with homogeneous designs. Heterogeneous designs consist of several subdesigns that are offered to different consumers and can be constructed with relative ease for a wide range of conjoint-choice models. Footnote 12

Ter Hofstede et al. [ 167 ] proposed a general model (finite mixture regression model) that includes the effects of discrete and continuous heterogeneity as well as self-stated and derived attribute importance in hybrid conjoint studies. As a departure from earlier studies, they treat self-stated importance as data rather than as prior information, and include them in the formulation of the likelihood thus helping them investigate the relationship of self-stated and derived importance at the individual level. Furthermore, the order constraints derived from the self-stated importances are “hard” constraints, ignoring the relative distance between importances and measurement error in the self-stated part-worths, which may result in the stated order differing stochastically from the “true” underlying order. Their study shows that including self-stated importance in the likelihood leads to much better predictions than does considering them as prior information. An excellent resource on HB methods in marketing and conjoint analysis can be found in Rossi et al. [ 147 ].

6.1.1 Suggested Directions for Future Research

It has not been conclusively demonstrated in what contexts consumer heterogeneity is better described by a continuous [ 4 ] or by a discrete distribution [ 44 ], pointing to a need for further research to resolve this issue (see also Ebbes et al. [ 55 ]). Still, we believe that the HB method is a preferred approach when a large number of part-worths need to be estimated compared to more classical methods of estimation that can use up a large number of degrees of freedom and where the likelihood function may have multiple maxima [ 84 , 138 ].

More research is required to examine the potential effects of distributional misspecification concerning the likelihood, prior, and hyper prior distributions in HB conjoint analyses (not just prior sensitivity).

6.2 D2. The Latent Class Approach

Market segmentation remains one of the most important uses for conjoint analysis based on the estimated attribute part-worths [ 31 , 105 , 168 , 186 ]. Historically, segments were developed in a rather disjointed two-step fashion (clustering after estimating individual-level conjoint part-worths). This resulted in various problems, for instance, in highly fractionized designs, the estimated individual-level part-worths are often unstable and are stochastic and quite different loss functions are optimized using these disjointed methods. In this light, there has been research dedicated to simultaneously performing this two-step approach more parsimoniously; for instance an early example includes the Q-factor analytic procedure that maximizes the predictive power of the derived segment-level utility function. DeSarbo and colleagues provide alternative cluster-wise regression based formulations for such benefit segmentation approaches utilizing conjoint analysis [ 42 ].

Following these deterministic cluster-wise approaches, a number of latent class or finite mixture-based solutions to simultaneously perform conjoint and market segmentation analysis had been developed. The advantages of these simultaneous procedures are that they employ stochastic frameworks involving mixtures of conditional distributions which allow for heuristic tests for the optimal number of segments (via AIC, BIC, CAIC, ICOMP, etc. heuristics), Footnote 13 fuzzy posterior probability of memberships that permit fractional membership in more than one market segment, and a stochastic approach that allows for computation of the standard errors of the estimated part-worths. Many such latent class conjoint procedures also allow for heteroscedasticity among groups of consumers as well as for variation within these groups’ responses. Interested readers are referred to several early articles by DeSarbo and colleagues (cited in DeSarbo and DeSarbo [ 42 ]). In the last decade, these authors develop a host of latent class models that can be applied to conjoint analysis, addressing the issue of segment identification. Chung and Rao [ 31 ] develop a comparability-based balance (COBA) model that accommodates bundle choices with any degree of heterogeneity among components (products) and incorporates consumer preference heterogeneity that can be used for segmentation and optimal bundle pricing.

Much of the early literature involved modeling heterogeneity through the use of individual-level traditional conjoint analysis. Bayesian conjoint analysis and latent class conjoint analysis had initially focused on the modeling of metric data. In more recent times, effort has been devoted to conjoint-choice experiments. This was motivated by the fact that conventional rating-based (metric) conjoint analysis depends on a consideration (rating) task that does not link directly to any behavioral theory. We feel that employing actual choice between alternatives is more realistic than the conventional approach of using mere artificial rankings and ratings. As such, we applaud the development of such latent class conjoint procedures for the analysis of choice data.

6.2.1 Suggested Directions for Future Research

Latent class models all typically assume that the respondent belongs to one and only one underlying segment allowing for the calculation of posterior probabilities. By definition, these posterior probabilities for each respondent sum to one, indicating a convex combination of these segment memberships. These individual-level predictions obtained from such finite mixture-based models tend to be rather poor depending upon the degree of separation of the centroids of the conditional segment-level support distributions and the within segment variation, thus limiting the range of the predictions. We encourage the development of new methods for improved prediction.

Segment identifiability remains a problem with such latent class segmentation procedures in conjoint analysis since individual differences in the estimated individual-level parameters are rarely well predicted by demographics, psychographics, etc. This same problem lies with respect to the estimated segment-level parameters as well. Even with explicit reparameterization of the mixing proportion via the concomitant approach, it is uncommon to be able to shed sufficient light on describing the derived market segments vis-à-vis traditional individual difference measurements. We encourage the development of new methods in improving segment identifiability.

Using the ideas of Hidden Markov Models [ 129 , 65 , 144 ], additional research is required to investigate the dynamic nature of such derived market segments including switching segment memberships over time, the evolution of different market segments over context or consumptive situations, and the time path of changing parameters.

6.3 D3. The Polyhedral Estimation Approach

Toubia et al. [ 171 ] proposed and tested a new “polyhedral” choice-based question-design method that adapts each respondent’s choice sets on the basis of previous answers by that respondent. Footnote 14 The simulations conducted suggest that polyhedral question design does well in many domains, particularly those in which heterogeneity and part-worth magnitudes are relatively large. In particular, the polyhedral algorithms hold potential when profile comparisons are more accurate than self-explicated importance measures and when respondent fatigue is a concern due to a large number of features. For example, in product development scenarios, managers may want to learn the incremental utility of a large number of features allowing them to screen several features quickly [ 138 ].

Toubia et al. [ 170 ] validated the polyhedral approach and found that it was superior to the fixed efficient design in both internal and external validity, and slightly better than the adaptive conjoint method. However, the polyhedral approach is highly sensitive to errors in the early choices. Despite mixed results of the polyhedral questions especially when response error is high, Toubia et al. [ 172 ] subsequently proposed and tested a probabilistic polyhedral method by recasting the polyhedral heuristic into a Bayesian framework which includes prior information in a natural, conjugate manner. This method shows potential to improve accuracy in high response-error domains by minimizing the expected size of the polyhedron (i.e., choice balance) and also by minimizing the maximum uncertainty on any combination of part-worths (i.e., post-choice symmetry). Evgeniou et al. [ 58 ] introduce methods from statistical learning theory to conjoint analysis that compares favorably to the polyhedral heuristic.

While, Toubia et al. [ 172 ] demonstrated improved accuracy in using probabilistic polyhedral method, the analytic-center estimation does not yet perform as well as the HB method. Abernethy et al. [ 1 ], using complexity control machine learning, demonstrate robustness to response errors inherent in adaptive choice which outperforms polyhedral estimation proposed by Toubia et al. [ 170 ]. More recently, Dzyabura and Hauser [ 54 ] developed and tested an active machine-learning algorithm to identify noncompensatory heuristic decision rules based on prior beliefs and respondent’s answers to previous questions. Currently, research that frames the fast polyhedral method in HB specification (GENPACE) has shown to outperform FastPACE under certain conditions [ 177 ].

6.3.1 Suggested Directions for Future Research

We suggest future conjoint scholars working with the polyhedral algorithm to combine self-explicated data within the framework of stated choice data to improve the estimation as shown by Toubia et al. [ 171 ] and Ter Hofstede et al. [ 167 ] in traditional conjoint analysis. Such self-explicated data can help constrain the rank order of part-worths and thereby shrink the polyhedral confidence region for estimated part-worths.

Combining analytic-center (AC) estimation with Bayesian methods may broaden the scope and applicability of the polyhedral algorithm when respondent heterogeneity and response accuracy in stated choice are both low. Also, the polyhedral ellipsoid algorithm can perhaps be further broadened to newer domains of application including situations marked by a lack of nondominance, choice balance, and symmetry—criteria that are presupposed in the current algorithm.

6.4 D4. Integrating Multiple Sources of Data

Based on existing research, conjoint analysis could also benefit substantially by combining multiple sources of data. Traditionally, preference measurement studies have relied on data provided explicitly by consumers during the preference measurement task. Both stated and revealed preference data provide information on the utility of offerings, and thus one source of data can be integrated as a covariate in a model of the other [ 82 ]. Further, Allenby et al. [ 6 ] recommend that information across datasets may be combined by forming a joint likelihood function with common parameters that will result in more precision. For example, stated preference data may require corrections for various response biases, while revealed preference data may require information controlling for contextual effects.

An interesting development by Ashok et al. [ 11 ] is the structural equation models (SEM) that integrate softer variables (e.g., attitudes) into binary and multinomial choice models to explain choice decisions. They compare the limited information model (without latent variables) in which factor scores for the exogenous latent variables are included in the utility function as error-free variables with the full information model with latent variables. In general, full information estimation methods yield structural parameter estimates that are significantly more precise than those obtained by using two-stage limited information approach where latent constructs are treated as error free instead of as random variables.

Furthermore, there is potential for combining stated preference data with auxiliary revealed preference data. For instance, researchers could look at qualitative and observational research techniques to capture response latencies, eye movement, and other psychosomatic patterns. Haaijer et al. [ 74 ] demonstrated that response time is related to preference and choice uncertainty such that shorter response times represent more certain choices. In a very recent study, Toubia et al. [ 173 ] conduct two eye-tracking studies (using Tobii 2150 tracker) to compare incentive-compatible conjoint poker with incentive-compatible choice-based conjoint. The assumption is that choice-based conjoint participants make choices based on a smaller subset of attributes resulting in decreased visual attention for a large proportion of attributes and levels.

The different approaches to modeling consumer preference (e.g., compositional model, decompositional model, subjective expected utility model, etc.) are based on the inherent assumption of traditional utility theory and attribute processing. However, consumer researchers for some time now have also established the power of affect, feelings, and emotions in consumer judgment, preference, and choice [ 136 ]. Unfortunately, not much research has been done to integrate the traditional utility-based paradigm with such affective responses in conjoint experiments. The concept of “attribute prominence” consisting of attribute importance and emotionality would better capture choice than merely using cognitive-based importance measures as earlier suggested by Luce et al. [ 118 ].

6.4.1 Suggested Directions for Future Research

A promising area in need of more work is the marriage of discrete choice models with latent variables such as attitudes and perceptions. Following Ashok et al. [ 11 ], we encourage more researchers to integrate latent constructs in discrete choice models such as attitude, satisfaction, service quality perception, and other widely used marketing-based perceptual constructs. A related area is the marriage of scanner-panel data with multinomial choice, where nonproduct attributes such as consumer attitudes and motivations and store level data may drive brand purchase along with product attributes [ 60 , 165 ].

Additional research should be aimed at understanding the underlying mechanism (rules and heuristics) that determines consumers’ decisions and develop measures of the decision process variables—decision problems, decision contexts, social situations, and individual factors.

We believe that integrating multiple sources of data in innovative ways can add to the reliability, validity, and generalizability of conjoint studies in the future. The integration of qualitative aspects and emotional reactions of consumers with stated preference data in forming preferences and choices is an important research avenue [ 43 ]. While aesthetic stimuli pose special challenge in designing a factorial design due to the difficulty of decomposing what is essentially unitary or holistic stimuli, researchers are encouraged to work creatively in harnessing the benefits of such auxiliary data.

Conjoint analysis provides an exacting measurement of consumer preferences, but to design a product or set marketing variables a firm must often do so in light of the actions and potential actions of its competitors. We are now beginning to see equilibrium (or nonequilibrium) models, which include the reactions of firms, competitors, and customers, coupled to conjoint analyses. One example is Kadiyali et al. [ 96 ]. More work needs to be done in this promising line of research.

7 (E) Managerial Issues Concerning Implementation

We now discuss selected implementation issues relevant for the manager including product optimization, market value of attribute improvement, optimal pricing, and product line decisions.

7.1 E1. Product Optimization

The primary goal of traditional conjoint analysis was to find a parsimonious manner of estimating consumer utility functions and deriving attribute (level) importances. In this effort, one could design a product with maximum utility whose attribute levels correspond to the highest estimated utility values. While the problem was first formulated as a zero–one integer programming model, a more general and thorough approach to product design optimization was developed by Green and colleagues with their Product Optimization and Selected Segment Evaluation (POSSE) procedures. Soon thereafter, efforts were directed to extend single-product design optimization heuristics to entire product lines introducing two objective functions (the buyer’s and seller’s welfare problem). This marked a critical development in product optimization research that triggered a flurry of research.

Another major advance in this field was the idea that consumers’ preference structures were dynamic rather than static (due to variety seeking, learning, and fatigue), which calls for models that can capture the dynamics and respondents heterogeneity (for a review, see Wittink and Keil [ 188 ]. More recent artificial intelligence and engineering optimization approaches to product line optimization using conjoint analysis include Belloni et al. [ 14 ], Wang et al. [ 182 ], Luo [ 119 ], and Michalek et al. [ 124 ]. Recently, some progress has been made by Luo et al. [ 120 ] wherein they propose a hierarchical Bayesian structural equation model by incorporating subjective characteristics along with objective attributes in new product design. Their results indicate that by collecting additional information about consumers’ perceptions of the subjective characteristics, the proposed model provides the product designer with a better understanding and a more accurate prediction of consumers’ product preferences compared to traditional conjoint models. We encourage more research in this area such as testing the virtual-reality prototypes [ 36 ], instead of physical prototypes, when attributes are large in number and therefore expensive.

7.1.1 Suggested Directions for Future Research

A line of research with promising potential is the area of improving preference measurement for really new products as opposed to incrementally new products. In attempts to improve preference measurement by building consumer knowledge, more research needs to be conducted to fully understand consumer inferential techniques in reducing uncertainty (i.e., consumer-initiated analogy generation and marketer-supported analogy). More needs to be done on how consumers think and learn about really new products pre-, during, and post adoption stages, and how we can modify measurement techniques to maximize the predictive accuracy of preference measurement.

We believe that attribute-based conjoint models are potentially limited and that further investigation should proceed at least as far as customer-ready prototypes for a spectrum of design concepts. The prototypes are likely to provide more accurate information on customer reactions and costs and more accurate information on the attribute levels achieved (rather than expected) with particular designs. One possible direction for future extension is to combine this with other related methods such as Neural Network Approaches and Genetic Algorithms to gain better prediction. See Chung and Rao [ 32 ] for modeling of unobserved attributes in experiential products using virtual expert model.

7.2 E2. Market Value of Attribute Improvement

Predicting performance in the marketplace and gaining insight into the value of design features are important goals of market research. One question of managerial relevance is whether or not attribute improvement can be measured in terms of cost-benefit analysis. In other words, given that an attribute improvement (positive change) always comes with a price increase (negative change), there is a trade-off involved in its impact on market share. Ofek and Srinivasan [ 131 ] show that the market value of an attribute improvement (MVAI) can be expressed as the ratio of the change in market share due to an improvement in attribute to the ratio of decrease in market share due to change in price. These authors tested this approach using five portable camera mount products described on five attributes each varied at three levels. They estimate the MVAI for each of the attributes and show that these have less bias than the commonly used attribute values computed by averaging the ratio of weights of attribute and price across individuals. They also demonstrated that profitability of attribute improvements decreased when factoring in competitive reactions. The firm should undertake attribute improvement if MVAI exceeds the cost of attribute improvement. To mimic a real-world situation, MVAI can incorporate choice set, competitive reactions, and heterogeneity of respondents and translate utilities into choice probabilities [ 138 ].

7.2.1 Suggested Directions for Future Research

Future research should pay more attention to the dynamic issue of consumer choice or preference (both before product design and before product launch), which means that studies should extend over multiperiods and respondents should be able to upgrade [ 138 , 100 ]. Also, research should be done after product diffusion (i.e. multiperiod analysis), as attributes’ importance will change as consumers gain more experience with the products as will the market value of the attribute.

Meta analyses in this area would be particularly desirable. More specifically, publishing research on tracking the monetary implications of pursuing optimal conjoint design implementations in different commercial scenarios would prove a great aid in advancing more applications of conjoint analyses.

7.3 E3. Optimal Pricing

Kohli and Mahajan [ 104 ] propose a model for determining the price that maximizes the profit of a product that has been screened based on share criterion. They do so by incorporating the effect of measurement and estimation error in demand estimates which in turn affects the price that maximizes profit. They model heterogeneity in individual reservation prices by assuming that the variance of the distribution is constant but the mean is normally distributed. Jedidi and Zhang [ 90 ] further develop this method to allow for the effect of new product introduction on category-level demand, and Jedidi et al. [ 92 ] describe a method for estimating consumer reservation prices for product bundles. Chung and Rao [ 31 ] evolve the issue of optimal pricing to the level of bundle choice models which employ attribute-based products (i.e., components) of a bundle as the ultimate unit of analysis in estimating the utility of the bundle. Reservation price for bundles is higher for attributes regarded as desirable or complimentary.

More recently, Iyengar et al. [ 87 ] describe a conjoint model for the multipart pricing of products and services. Given that for many product and service categories there is a two-way dependence of price and consumption (fixed fee and usage-based fee), Iyengar et al. [ 87 ] incorporate the effects of consumption on consumer choice and the uncertainty of service usage (by using a quadratic utility function). A benefit of their model is its ability to infer consumption at different prices from choice data which can aid marketers in their market share maximization objectives.

Ding et al. [ 50 ] demonstrate that consumers demonstrate two behavioral regularities in relation to how their utility functions depend on the role of price: consumers infer quality information from a product’s price and they have a reference price for a given product. Consumer heterogeneity is captured through an individual-specific reference point and an individual-specific information coefficient. They demonstrate that the classic economic model where price serves the allocative purpose is more relevant for inexperienced or uninvolved customers. On the other hand, price maximally serves as an informational price cuing quality where customers are the most involved. This piece of research is one of the pioneering first steps in integrating behavioral regularities into classic utility models in pricing research. Kannan et al. [ 98 ], through an online choice experiment on digital versus print products, propose a model to account for customers’ perceptions of substitutability or complementarity of content forms in developing pricing policies for digital products. Research on product line extensions has traditionally treated this issue as substitutes, although it is possible that customers may perceive digital products as imperfect substitutes or even complements to printed products. Bundling and pricing strategies are determined by capturing customers’ heterogeneity in their perceptions of substitutability and complementarity by estimating parameters of the model using a finite mixture (FM) model.

7.3.1 Suggested Directions for Future Research

Along the lines of Iyengar et al. [ 87 ], future research can examine computationally efficient methods for optimal selection of product features and prices. There is also potential for factoring in the effect of competitive actions and reactions on multipart pricing.

Future researchers can look into additional behavioral regularities built into the utility model such as a reflexive shape around the reference point and the effect of dynamic competition. This would be a useful area for the application of game theoretic models employing alternative strategies and competitive scenarios.

7.4 E4. Product Line Decisions

The optimal product line design problem belongs to the class of NP-hard combinatorial optimization problems. A number of optimization algorithms have been applied to solve such difficult problems including dynamic programming, beam search, genetic algorithms, and Lagrangian relaxation with branch and bound [ 12 , 23 ]. More recently, alternative heuristics have been devised employing conjoint and choice models. Michalek et al. [ 124 ] recently presented a unified methodology for product line optimization that coordinates positioning and design models to achieve realizable firm-level optima. Their procedure incorporates a general Bayesian representation of consumer preference heterogeneity, and manages attributes over a continuous domain to alleviate issues of combinatorial complexity using conjoint based consumer choice data. Tsafarakis et al. [ 174 ] devise particle swarm optimization technology for the problem of optimal product line design and employ a Monte Carlo simulation to favorably compare its performance to the use of genetic algorithms. In addition, these authors use concepts from game theory to illustrate how the proposed algorithm can be extended to incorporate retaliatory actions from competitors using Nash equilibrium concepts.

7.4.1 Suggested Directions for Future Research

Future research in this area should extend such models beyond linear and continuous cost functions, to accommodate mixed level product attributes (discrete and continuous), to handle category expansion and pioneering advantages, and allow for the enactment of various designated offensive and defensive strategies.

It would also be useful to extend such computer science-based procedures to accommodate multiple objectives for optimization in conjoint applications.

8 Conclusion

From the rigorous psychometric tradition from which conjoint analysis has evolved, a plethora of advances have been made. In this manuscript, we have attempted to integrate several substantive issues of interest in conjoint analysis within an organizing framework that impacts major stakeholders (i.e., researcher, respondent, and manager). For each of the five categories in our framework, we summarize recent developments in the field, provide some critical insights, and present suggested directions for future research. We hope that conjoint scholars will gainfully employ this organizing framework as a repository for drawing additional new insights and conducting future research. We believe that research in conjoint continues to be vibrant and the recent advances, developments, and directions discussed in this paper will contribute to the realization of the tremendous potential of conjoint analysis.

In conclusion, our paper makes several contributions to the literature (including the recent book by Rao [ 139 ]). First, our review incorporates an organizing framework based on the behavioral and theoretical processes underlying several issues related to the researcher, the respondent, and the manager in conjoint analysis. We have an expanded and provided recent coverage of the behavioral and theoretical underpinnings (see section A) that sets the tone for the rest of the review. Second, our framework allocates adequate attention to critical issues surrounding the three major stakeholders: the researcher, the respondent, and the manager. Third, we cite publications from major marketing and nonmarketing journals across disciplines. Fourth, our paper also sets a comprehensive research agenda going forward, 55 research directions in total, which can be leveraged for future development of conjoint analysis methodology. Finally, we believe that a review paper on conjoint analysis will be able to draw wide readership and citation by scholars in the future, thereby enhancing the impact factor of this journal.

The part-worth conjoint analysis model is basic and may be represented by the following formula: \( {U}_x=\sum_{i=1}^m\sum_{j=1}^{k_i}{\alpha}_{ij}{x}_{ij} \) where \( {U}_x \)  = overall utility of an alternative; \( {\alpha}_{ij} \) = the part-worth contribution or utility associated with the j th level ( j , j  = 1, 2…. ki ) of the i th attribute ( i , i  = 1, 2…. m ); \( {k}_i \)  = number of levels of attribute I ; \( m \)  = number of attributes; \( {x}_{ij} \)  = 1 if the j th level of the i th attribute is present and = 0 otherwise.

These include Green and Srinivasan [ 71 , 72 ], Wittink and Cattin [ 187 ], Carroll and Green [ 25 ], Hauser and Rao [ 75 ], and Rao [ 138 , 139 ].

Recent developments in tools in psychology including functional imaging and neural recordings, process tracing tools, and modeling tools such as mediation and multilevel analysis have benefitted this research stream. For instance, process models consider intervening variables and intermediate stages between the start and end of the decision by incorporating additional external search information and internal memory-based information.

The beta-delta model explains greater discounting of future outcomes when immediate rewards are available than when all rewards are in the future, by an exponential delta process that always operates and an additional exponential beta process that only operates when immediate rewards are present. For instance, in decisions from descriptions, certainty in the probability dimension and immediacy on the delay dimension are given extra attention, and consequently decision weight, as captured by prospect theory and Laibson’s beta-delta model of time discounting [ 108 ].

The function is typically, U  =  ΣXβ , where X ’s are attributes or functions of attributes such as X 1 X 2 .

Liu and Arora [ 115 ] found asymmetric effects in design efficiency loss. When the true model is conjunctive, compensatory designs have significant loss of design efficiency. However, when the true model is compensatory, the efficiency loss from using a conjunctive design is significantly lower.

Cue diagnosticity is an information processing technique based on metacognitive insights about past inferential accuracy that helps in distinguishing between two alternatives. TTB is an inferential strategy based on memory retrieval mimicking lexicographic decision rule in choice using the most diagnostic cue.

For instance, Chevalier and Mayzlin [ 29 ] find that differences in the number of ratings (volume) and the average rating (valence) across online book retailers (Amazon and Barnes and Noble.com) affected relative sales. Similar results have been found wherein online customer movie ratings are related to future box office revenues [ 41 ].

For an interesting online study of user engagement conducted by Yahoo using web-based user and content data (click through rate) and tensor segmentation technique, see [ 30 ].

Experimental choice analysis often combines discrete choice responses, a logit model, fractional and factorial designs [ 25 ]. Interested readers can refer to some of the seminal papers in choice-based logit models reviewed earlier [ 138 , 75 ].

In one of the earlier studies, Cattin et al. [ 26 ] employed a Bayesian procedure to improve the prediction of holdout profiles by using self-stated utilities to derive a prior distribution. This prior distribution was used in the estimation of the individual-level part-worths from the full-profile evaluations. Subsequent to their pioneering work, several noteworthy studies have estimated the importances from full-profile data under various real-world constraints derived from the order information in the self-stated data thereby improving the predictive performance of the model (see reviews by Hauser and Rao [ 75 ] and Rao [ 138 ]).

In HB practical applications, there usually is no attempt to optimize the design blocking, so there is no reason to expect the particular trade-offs individual subjects see to provide a meaningful basis for a Bayesian update of the priors provided by the population means. However, Sawtooth Software assigns blocks by drawing randomly from the full design for each subject resulting in better HB estimates. For asymmetric designs, random designs can be more efficient overall than purely orthogonal designs.

AIC is Akaike’s Information Criterion, BIC is Bayesian Information Criterion, CAIC is Consistent Akaike’s Information Criterion, and ICOMP is Information Complexity. For technical details of segment retention criteria, the reader is referred to Andrews and Currim [ 7 ].

Polyhedral “interior-point” algorithms (Fast Polyhedral Adaptive Conjoint Estimation or FastPACE) design questions that quickly reduce the range of feasible part-worths that are consistent with the respondent’s choices. The estimation methods employed are hierarchical Bayes and “analytic center”, a new estimation procedure that is a by-product of polyhedral question design. The analytic center is the point that minimizes the geometric mean of the distances to the faces of the polyhedron thereby yielding a close approximation to the center of the polyhedron.

Abernethy J, Evgeniou T, Toubia O, Vert J-P (2008) Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Trans Knowl Data Eng 20(2):145–155

Google Scholar  

Adamowicz W, Bunch D, Cameron TA, Dellaert BGC, Hanneman M, Keane M et al (2008) Behavioral frontiers in choice modeling. Mark Lett 19(3–4):215–228

Alba JW, Cooke ADJ (2004) When absence begets inference in conjoint analysis. J Mark Res 41(4):382–387

Allenby GM, Ginter JL (1995) Using extremes to design products and segment markets. J Mark Res 32(4):392–403

Allenby GM, Neeraj A, Ginter JL (1995) Incorporating prior knowledge into the analysis of conjoint studies. J Mark Res 35(2):152–162

Allenby GM, Fennell G, Huber J, Eagle T, Gilbride T, Horsky D, Kim J, Lenk PJ, Johnson R, Ofek E, Orme B, Otter T, Walker J (2005) Adjusting choice models to better predict market behavior. Mark Lett 16(3–4):197–208

Andrews RL, Currim IS (2003) A comparison of segment retention criteria for finite mixture logit models. J Mark Res 40(2):235–243

Andrews RL, Ansari A, Currim IS (2002) Hierarchical Bayes versus finite mixture conjoint analysis models: a comparison of fit, prediction, and partworth recovery. J Mark Res 39(1):87–98

Ariely D, Loewenstein G, Prelec D (2003) Coherent arbitrariness: stable demand curves without stable preferences. Quant J Econ 118:73–105

Arora N, Allenby GM (1999) Measuring the influence of individual preference structures in group decision making. J Mark Res 36(4):476–487

Ashok K, Dillon WR, Yuan S (2002) Extending discreet choice models to incorporate attitudinal and other latent variables. J Mark Res 39(1):31–46

Balakrishnan PVS, Gupta R, Jacob VS (2006) An investigation of mating and population maintenance strategies in hybrid genetic heuristics for product line designs. Comput Oper Res 33(3):639–659

Bartels DM, Medin DL (2007) Are morally motivated decision makers insensitive to the consequences of their choices? Psych Sci 18:24–28

Belloni A, Freund R, Selove M, Simester D (2008) Optimizing product line designs: efficient methods and comparisons. Manag Sci 54(9):1544–1552

Bettman JP, Luce MF, Payne JW (1998) Constructive consumer choice processes. J Consum Res 25(3):187–217

Bond SD, Carlson KA, Meloy MG, Russo JE, Tanner RJ (2007) Information distortion in the evaluation of a single option. Organ Behav Hum Decis Process 102:240–254

Botti S, Broniarczyk S, Häubl G, Hill R, Huang Y, Kahn B, Kopalle P, Lehmann D, Urbany J, Wansink B (2008) Choice under restrictions. Mark Lett 19(3–4):183–199

Bradlow ET (2005) Current issues and a wish list for conjoint analysis. Appl Stochast Models Bus Ind 21(4–5):319–323

Bradlow ET, Park Y-H (2007) Bayesian estimation of bid sequences in Internet auctions using a generalized record-breaking model. Mark Sci 26(2):218–229

Bradlow ET, Hu Y, Ho T-H (2004) A learning-based model for imputing missing levels in partial conjoint profiles. J Mark Res 41(4):369–381

Burgess L, Street DJ (2005) Optimal designs for choice experiments with asymmetric attributes. J Stat Plan Infer 134:288–301

Camerer CF (2005) Three cheers—psychological, theoretical, empirical—for loss aversion. J Mark Res 42(2):129–133

Camm JD, Cochran JJ, Curry DJ (2006) Conjoint optimization: an exact branch-and-bound algorithm for the share-of-choice problem. Manag Sc 52(3):435–447

Campbell D, Hensher DA, Scarpa R (2011) Non-attendance to attributes in environmental choice analysis: a latent class specification. J Environ Plan Manag 54:2061–1076

Carroll DJ, Green PE (1995) Psychometric methods in marketing research: part I, conjoint analysis. J Mark Res 32(4):385–391

Cattin P, Gelfand AE, Danes J (1983) A simple Bayesian procedure for estimation in a conjoint model. J Mark Res 20(1):29–35

Cawley J (2008) Contingent valuation analysis of willingness to pay to reduce childhood obesity. Econ Hum Biol 6:281–292

Chandon P, Morwitz VG, Reinartz WJ (2004) The short and long-term effects of measuring intent to repurchase. J Consum Res 31:566–572

Chevalier J, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354

Chu W, Park S, Beaupre T, Motgi N, Phadke A, Chakraborty S, Zachariah J (2009) A case study of behavior-driven conjoint analysis on Yahoo! front page today module (2009) Proceedings of KDD

Chung J, Rao VR (2003) A general choice models for bundles with multiple-category products: application to market segmentation and optimal pricing for bundles. J Mark Res 40(2):115–130

Chung J, Rao VR (2012) A general consumer preference model for experience products: application to internet recommendation services. J Mark Res 49(2):289–305

Cohen S, Neira L (2003) Measuring preference for product benefits across countries: Overcoming scale usage bias with maximum difference scaling. ESOMAR 2003 Latin Amer. Conf. Proceedings, Amsterdam

Connolly T, Zeelenberg M (2002) Regret in decision making. Psych Sci 11:212–216

Dahan E (2007) Conjoint adaptive ranking database system. Working paper. University of California at Los Angeles, Los Angeles

Dahan E, Srinivasan V (2000) The predictive power of internet-based product concept testing using visual depiction and animation. J Prod Innov Manag 17(2):99–109

De Bruyn A, Liechty JC, Huizingh EKRE, Lilien GL (2008) Offering online recommendations with minimum customer input through conjoint-based decision aids. Mark Sci 27(3):443–460

de Jong MG, Lehmann DR, Netzer O (2012) State-dependence effects in surveys. Mark Sci 31(5):838–854

De Wilde E, Cooke ADJ, Janiszewski C (2008) Attentional contrast during sequential judgments: a source of the number-of-levels effect. J Mark Res 45(4):437–449

Dellaert BGC, Stremersch S (2005) Marketing mass-customized products: striking a balance between utility and complexity. J Mark Res 42(2):219–227

Dellarocas C, Zhang M, Awad NF (2007) Exploring the value of online product reviews in forecasting sales: the case of motion pictures. J Interact Mark 21(4):23–45

DeSarbo WS, DeSarbo C (2007) A generalized normative segmentation methodology employing conjoint analysis. In: Gustafsson A, Herrmann A, Huber F (eds) Conjoint measurement: methods and applications, 4th edn. Springer, New York, pp 321–346

DeSarbo WS, Wu J (2001) The joint spatial representation of multiple variable batteries collected in marketing research. J Mark Res 38(2):244–253

DeSarbo WS, Ansari A, Chintagunta P, Himmelberg C, Jedidi K, Johnson R, Kamakura W, Lenk P, Srinivasan K, Wedel M (1997) Representing heterogeneity in consumer response models 1996 choice conference participants. Mark Lett 8(3):335–348

DeSarbo WS, Fong DKH, Liechty J, Chang-Coupland J (2005) Evolutionary preference/utility functions: a dynamic perspective. Psychometrika 70(1):179–202

Ding M (2007) An incentive-aligned mechanism for conjoint analysis. J Mark Res 44(2):214–223

Ding M, Eliashberg J (2008) A dynamic competitive forecasting model incorporating dyadic decision making. Manag Sci 54(4):820–834

Ding M, Grewal R, Liechty J (2005) Incentive-aligned conjoint analysis. J Mark Res 42(1):67–82

Ding M, Park Y-H, Bradlow ET (2009) Barter markets for conjoint analysis. Manag Sci 55(6):1003–1017

Ding M, Ross WT Jr, Rao VR (2010) Price as an indicator of quality: implications for utility and demand functions. J Retail 86(1):69–84

Dong S, Ding M, Huber J (2010) A simple mechanism to incentive-align conjoint experiments. Int J Res Mark 27:25–32

Dougherty MRP, Sprenger A (2006) The influence of improper sets of information on judgment: how irrelevant information can bias judged probability. J Exp Psych Gen 135:262–281

Dyachenko T, Naylor R, Allenby G (2013) Models of sequential evaluation in best-worst choice tasks, Proceedings of the Sawtooth Software Conference, Sawtooth Software Inc

Dzyabura D, Hauser JR (2011) Active machine learning for consideration heuristics. Mark Sci 30(5):801–819

Ebbes P, Liechty J, Grewal R (2014) Attribute-level heterogeneity. Manag Sci. doi: 10.1287/mnsc.2014.1898

Elrod T, Häuble G, Tipps SW (2012) Parsimonious structural equation models for repeated measures data, with application to the study of consumer preferences. Psychometrika 77(2):358–387

Evans JSBT (2008) Dual-processing accounts of reasoning, judgment, and social cognition. Annual Rev Psych 59:255–278

Evgeniou T, Boussios C, Zacharia G (2005) Generalized robust conjoint estimation. Mark Sci 24(3):415–429

Evgeniou T, Pontil M, Toubia O (2007) A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Mark Sci 26(6):805–818

Feit ED, Beltramo MA, Feinberg FM (2010) Reality check: combining choice experiments with market data to estimate the importance of product attributes. Manag Sci 56(5):785–800

Fiebig DG, Keane MP, Louviere J, Wasi N (2010) The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Mark Sci 29(3):393–421

Finn A, Louviere JJ (1992) Determining the appropriate response to evidence of public concern: the case of food safety. J Public Policy Mark 11(2):12–25

Fong DKH, DeSarbo WS (2012) A Bayesian methodology for simultaneously detecting and estimating regime change points and variable selection in multiple regression models for marketing research. Quant Mark Econ 5(4):293–314

Fong DKH, Ebbes P, DeSarbo WS (2012) A heterogeneous Bayesian regression model for cross-sectional data involving a single observation per response unit. Psychometrika 77(2):293–314

Frühwirth-Schnatter S (2006) Finite mixtures of regression models. Finite mixture and Markov switching models. Springer, New York, pp 241–275

Ghose S, Rao VR (2007) A choice model of bundles features and meta-attributes: an application to product design. Working Paper, Cornell University

Gilbride TJ, Allenby GM (2004) A choice model with conjunctive, disjunctive, and compensatory screening rules. Mark Sci 23(3):391–406

Godes D, Mayzlin D (2004) Using online conversations to measure word of mouth communication. Mark Sci 23(4):545–560

Godes D, Mayzlin D, Chen Y, Das S, Dellarocas C, Pfeiffer B, Libai B, Sen S, Shi M, Verlegh P (2005) The firm’s management of social interactions. Mark Lett 16(3–4):415–428

Goldfarb A, Ho T-H, Amaldoss W, Brown AL, Chen Y, Cui TH, Galasso A, Hossain T, Hsu M, Lim N, Xiao M, Yang B (2012) Behavioral models of managerial decision-making. Mark Lett 23(2):405–442

Green PE, Srinivasan V (1978) Conjoint analysis in consumer research: issues and outlook. J Consum Res 5(2):103–123

Green PE, Srinivasan V (1990) Conjoint analysis in marketing: new developments with implications for research and practice. J Mark 54(4):3–19

Gregan-Paxton J, John DR (1997) Consumer learning by analogy: a model of internal knowledge transfer. J Consum Res 24(3):266–284

Haaijer R, Kamakura WA, Wedel M (2000) Response latencies in the analysis of conjoint choice experiments. J Mark Res 37(3):376–382

Hauser JR, Rao VR (2003) Conjoint analysis, related modeling, and applications. In: Wind Y, Green PE (eds) Marketing research and modeling: progress and prospects: a tribute to Paul E. Green. Kluwer, Norwell

Hauser JR, Toubia O, Evgeniou T, Befurt R, Dzyabura D (2010) Disjunctions of conjunctions, cognitive simplicity, and consideration sets. J Mark Res 47(2):376–382

Hertwig R, Barron G, Weber EU, Erev I (2004) Decisions from experience and the effect of rare events in risky choice. Psych Sci 15:534–539

Higgins ET (2005) Value from regulatory fit. Psych Sci 14:209–213

Hoeffler S (2003) Measuring preferences for really new products. J Mark Res 40(4):406–420

Hogarth RM, Karelaia N (2007) Heuristic and linear models of judgment: matching rules and environments. Psych Rev 114:733–758

Horowitz JL, Louviere JJ (1995) What is the role of consideration sets in choice modeling? Int J Res Mark 12(May):39–54

Horsky D, Nelson P, Posavac SS (2004) Stating preference for the ethereal but choosing the concrete: how the tangibility of attributes affects attribute weighting in value elicitation and choice. J Consum Psych 14(1–2):132–140

Hsua DK, Haynie JM, Simmons SA, McKelvie A (2014) What matters, matters differently: a conjoint analysis of the decision policies of angel and venture capital investors. Ventur Cap An Int J Entrep Finan 16(1):1–25

Huber J, Train K (2001) On the similarity of classical and Bayesian estimates of individual mean part-worths. Mark Lett 12(3):259–269

Huber J, Zwerina K (1996) The importance of utility balance in efficient choice designs. J Mark Res 33(3):307–317

Hutchinson JW, Zauberman G, Meyer R (2010) On the interpretation of temporal inflation parameters in stochastic models of judgment and choice. Mark Sci 29(1):23–31

Iyengar R, Jedidi K, Kohli R (2008) A conjoint approach to multipart pricing. J Mark Res 45(2):195–210

Iyengar R, Van den Bulte C, Valente TW (2011) Opinion leadership and social contagion in new product diffusion. Mark Sci 30(2):195–212

Jedidi K, Kohli R (2005) Probabilistic subset—conjunctive models for heterogeneous consumers. J Mark Res 42(4):483–494

Jedidi K, Zhang JZ (2002) Augmenting conjoint analysis to estimate consumer reservation price. Manag Sci 48(10):1350–1368

Jedidi K, Kohli R, DeSarbo WS (1996) Consideration sets in conjoint analysis. J Mark Res 33(3):364–372

Jedidi K, Jagpal S, Manchanda P (2003) Measuring heterogeneous reservation prices for product bundles. Mark Sci 22(1):107–130

Johannesson M, Blomquist GC, Blumenschen K, Johansson PO, Liljas B, O’Connor RM (1999) Calibrating hypothetical willingness to pay responses. J Risk Uncertain 18(1):21–32

Johnson JG, Busemeyer JR (2005) A dynamic, stochastic, computational model of preference reversal phenomena. Psych Rev 112:841–861

Johnson EJ, Haubl G, Keinan A (2007) Aspect of endowment: a query theory of value construction. J Exp Psych Learn Mem Cogn 33:461–474

Kadiyali V, Sudhir K, Rao VR (2001) Structural analysis of competitive behavior: new empirical industrial organizational methods in marketing. Int J Res Mark 18(1, 2):161–186

Kahneman D (2003) Maps of bounded rationality: a perspective on intuitive judgment and choice. In: Frangsmyr T (ed) Les Prix Nobel: The Nobel Prizes 2002. Nobel Foundation, Stockholm, pp 449–489

Kannan PK, Pope BK, Jain S (2009) Pricing digital content product lines: a model and application for the national academies press. Mark Sci 28(4):620–636

Kanninen B (2002) Optimal design for multinomial choice experiments. J Mark Res 39:214–227

Kim SS, Malhotra NK (2005) A longitudinal model of continued is use: an integrative view of four mechanisms underlying postadoption phenomena. Manag Sci 51(5):741–755

Kim H-J, Park Y-H, Bradlow ET, Ding M (2014) PIE: a holistic preference concept and measurement model. J Mark Res 51(1):335–351

Kivetz R, Netzer O, Srinivasan V (2004) Alternative models for capturing the compromise effect. J Mark Res 41(3):237–257

Kohli R, Jedidi K (2007) Representation and inference of lexicographic preference models and their variants. Mark Sci 26(3):380–399

Kohli R, Mahajan V (1991) A reservation-price model for optimal pricing of multiattribute products in conjoint analysis. J Mark Res 28(3):347–354

Krieger AM, Green PE (1996) Modifying cluster-based segments to enhance agreement with an exogenous response variable. J Mark Res 33(3):351–363

Kuhfeld WF (2005) Marketing research methods in SAS: experimental design, choice, conjoint, and graphical techniques. SAS Institute, Cary

Lachaab M, Ansari A, Jedidi K, Trabelsi A (2006) Modeling preference evolution in discrete choice models: a Bayesian state-space approach. Quant Mark Econ 4(1):57–81

Laibson D (1997) Golden eggs and hyperbolic discounting. Quant J Econ 112:443–477

Lee MD, Cummins TDR (2004) Evidence accumulation in decision making: unifying the “Take The Best” and the “Rational” models. Psych Bull Rev 11:343–352

Levav J, Heitmann M, Herrmann A, Iyengar SS (2010) Order in product customization decisions: evidence from field experiments. J Polit Econ 118(2):274–299

Lichtenstein S, Slovic P (2006) The construction of preference. Cambridge University Press, London

Liechty JC, Ramaswamy V, Cohen SH (2001) Choice menus for mass customization: an experimental approach for analyzing customer demand with an application to a web-based information service. J Mark Res 38(2):183–196

Liechty JC, Fong DKH, DeSarbo WS (2005) Dynamic models incorporating individual heterogeneity: utility evolution in conjoint analysis. Mark Sci 24(2):285–293

Liu Q, Arora N (2011) Efficient choice designs for a consider-then-choose model. Mark Sci 30(2):321–338

Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psych Bull 127:267–286

Louviere JJ, Meyer RJ (2007) Formal choice models of informal choices: what choice modeling research can (and can’t) learn from behavioral theory. In: Malhotra NK (ed) Review of marketing research. Sharpe, New York, pp 3–32

Louviere JJ, Street D, Burgess L, Wasi N, Islam T, Marley AAJ (2008) Modeling the choices of individuals decision makers by combining efficient choice experiment designs with extra preference information. J Choice Model 1(1):128–163

Luce MF, Payne JW, Bettman JR (1999) Emotional trade-off difficulty and choice. J Mark Res 36(2):143–159

Luo L (2011) Product line design for consumer durables: an integrated marketing and engineering approach. J Mark Res 48(1):128–139

Luo L, Kannan PK, Ratchford BT (2008) Incorporating subjective characteristics in product design and evaluations. J Mark Res 45(2):182–194

Marley AAJ, Louviere JJ (2005) Some probabilistic models of best, worst, and best-worst choices. J Math Psych 49(6):464–480

Marley AAJ, Flynn TN, Louviere JJ (2005) Probabilistic models of set-dependent and attribute-level best–worst choice. J Math Psych 52(5):281–296

McKenzie CRM, Nelson JD (2003) What a speaker’s choice of frame reveals: reference points, frame selection, and framing effects. Psychon Bull Rev 10(3):596–602

Michalek JJ, Ebbes P, Adigüzel F, Feinberg FM, Papalambros PY (2011) Enhancing marketing with engineering: optimal product line design for heterogeneous markets. Int J Res Marketing 28(1):1–12

Miller KM, Hofstetter R, Krohmer H, Zhang ZJ (2011) How should consumers’ willingness to pay be measured? an empirical comparison of state-of-the-art approaches. J Mark Res 48(1):172–184

Morikawa T, Ben-Akiva M, McFadden D (2002) Discrete choice models incorporating revealed preferences and psychometric data. Adv Econ 16:29–55

Narayan V, Rao VR, Saunders C (2011) How peer influence affects attribute preferences: a Bayesian updating mechanism. Mark Sci 30(2):368–384

Netzer O, Srinivasan V (2011) Adaptive self-explication of multi-attribute preferences. J Mark Res 48(1):140–156

Netzer O, Lattin J, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Mark Sci 27(2):185–204

Netzer O, Toubia O, Bradlow ET, Dahan E, Evgeniou T, Feinberg FM, Feit EM, Hui SK, Johnson J, Liechty JC, Orlin JB, Rao VR (2008) Beyond conjoint analysis: advances in preference measurement. Mark Lett 19(3–4):337–354

Ofek E, Srinivasan V (2002) How much does the market value an improvement in a product attribute? Mark Sci 22(3):398–411

Pachur T, Hertwig R (2006) On the psychology of the recognition heuristic: retrieval primacy as a key determinant of its use. J Exp Psych Learn Mem Cogn 32:983–1002

Park JH, MacLachlan DL (2008) Estimating willingness to pay with exaggeration bias-corrected contingent valuation method. Mark Sci 27(4):691–698

Park Y-H, Ding M, Rao VR (2008) Eliciting preference for complex products: a web-based upgrading method. J Mark Res 45(5):562–574

Parker RJ, Schrift RY (2011) Rejectable choice sets: how seemingly irrelevant no-choice options affect consumer decision processes. J Mark Res 48(4):840–854

Pham MT, Cohen JB, Pracejus JW, Hughes GD (2001) Affect monitoring and the primacy of feelings in judgment. J Consum Res 28(2):167–188

Raghavarao D, Wiley JB, Chitturi P (2010) Choice-based conjoint analysis: models and designs. Chapman and Hall/CRC, New York

Rao VR (2008) Developments in conjoint analysis. In: Wierenga B (ed) Handbook of marketing decision models. Springer, New York, pp 23–55

Rao VR (2014) Applied conjoint analysis. Springer, New York

Rao VR, Kartono B, Su M (2008) Methods for handling massive numbers of attributes in conjoint analysis. Rev Mark Res 5:104–129

Reed A II (2004) Activating the self-importance of consumer selves: exploring identity salience effects on judgments. J Consum Res 31(2):286–295

Richter T, Spath P (2006) Recognition is used as one cue among others in judgment and decision making. J Exp Psych Learn Mem Cogn 32:150–162

Roe RM, Busemeyer JR, Townsend JT (2001) Multiattribute decision field theory: a dynamic connectionist model of decision making. Psych Rev 108:370–392

Romero J, van der Lans R, Wierenga B (2013) A partially hidden Markov model of customer dynamics for clv measurement. J Interact Mark 27(3):185–208

Rooderkerk RP, Van Heerde HJ, Bijmolt THA (2011) Incorporating context effects into a choice model. J Mark Res 48(4):767–780

Rose JM, Bliemer MCJ, Hensher DA et al (2008) Designing efficient stated choice experiments in the presence of reference alternatives. Transp Res Part B Methodol 42(4):395–406

Rossi PE, Allenby GM, McCulloch R (2005) Bayesian statistics and marketing. Wiley, West Sussex. doi: 10.1002/0470863692

Ruan S, MacEachern SN, Otter T, Dean AM (2008) The dependent Poisson race model and modeling dependence in conjoint choice experiments. Psychometrika 73(2):261–288

Rutz OJ, Sonnier GP (2011) The evolution of internal market structure. Mark Sci 30(2):274–289

Salisbury LC, Feinberg FM (2010) Alleviating the constant stochastic variance assumption in decision research: theory, measurement, and experimental test. Mark Sci 29(1):1–17

Sandor Z, Wedel M (2001) Designing conjoint choice experiments using managers’ prior beliefs. J Mark Res 38(4):430–444

Sandor Z, Wedel M (2002) Profile construction in experimental choice designs for mixed logit models. Mark Sci 21(4):455–475

Sandor Z, Wedel M (2005) Heterogeneous conjoint choice designs. J Mark Res 42(2):210–218

Scarpa R, Thiene M, Train KE (2008) Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps. Am J Agric Econ 90:994–1010

Scarpa R, Gilbride TJ, Campbell D, Hensher DA (2009) Modelling attribute non-attendance in choice experiments for rural landscape valuation. Eur Rev Agric Econ 36(2):151–174

Schlosser AE, Shavitt S (2002) Anticipating discussion about a product: rehearsing what to say can affect your judgments. J Consum Res 29(1):101–115

Shi SW, Wedel M, Pieters FGM (R) (2012) Information acquisition during online decision making: a model-based exploration using eye-tracking data. Manag Sci 59(5):1009–1026

Simon D, Krawczyk DC, Holyoak KJ (2004) Construction of preferences by constraint satisfaction. Psych Sci 15:331–336

Sonnier GP, McAlister L, Rutz OJ (2011) A dynamic model of the effect of online communications on firm sales. Mark Sci 30(4):702–716

Srinivasan S, Park CS (1997) Surprising robustness of the self-explicated approach to customer preference structure measurement. J Mark Res 34(2):286–291

Street DJ, Burgess L (2004) Optimal and near-optimal pairs for the estimation of effects in 2-level choice experiments. J Stat Plan Inf 118:185–199

Street DS, Burgess L (2007) The construction of optimal stated choice experiments: theory and methods. Wiley-Interscience, Hoboken

Street DJ, Bunch DS, Moore BJ (2001) Optimal designs for 2 k paired comparison experiments. Commun Stat– Theory Method 30(10):2149–2171

Stuttgen P, Boatwright P, Monroe RT (2012) A satisficing choice model. Mark Sci 31(6):878–899

Swait J, Andrews RL (2003) Enriching scanner panel models with choice experiments. Mark Sci 22(4):442–460

Swait J, Louviere JJ, Anderson D (1995) Best-worst conjoint: a new preference elicitation method to simultaneously identify overall attribute importance and attribute level partworths. Working Paper, University of Florida

Ter Hofstede FK, Kim Y, Wedel M (2002) Bayesian prediction in hybrid conjoint analysis. J Mark Res 39(2):253–261

Ter Hofstede FK, Wedel M, Steenkamp J-BEM (2002) Identifying spatial segments in international markets. Mark Sci 21(2):160–177

Toubia O, Hauser JR (2007) On managerially efficient experimental designs. Mark Sci 26(6):850–858

Toubia O, Simester DJ, Hauser JR, Dahan E (2003) Fast polyhedral adaptive conjoint estimation. Mark Sc 22(3):273–303

Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J Mark Res 41(1):116–131

Toubia O, Hauser JR, Garcia R (2007) Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: theory and application. Mark Sci 26(5):596–610

Toubia O, de Jong MG, Steiger D, Fuller J (2012) Measuring consumer preferences using conjoint poker. MarkeSci 31(1):138–156

Tsafarakis S, Marinakis Y, Matsatsinis N (2010) Particle swarm optimization for optimal product line design. Internat J Res in Mark 27(4):13–32

Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5:297–323

Tversky A, Koehler DJ (1994) Support theory: a non-extensional representation of subjective probability. Psych Rev 101:547–567

Vadali S, Liechty JC, Rangaswamy A (2006) Generalized hierarchical Bayes estimation for polyhedral conjoint analysis. Working Paper , Pennsylvania State University

Venkatraman S, Aloysius JA, Davis FD (2006) Multiple prospect framing and decision behavior: the mediational roles of perceived riskiness and perceived ambiguity. Organ Behav Hum Decis Process 101:59–73

Verlegh PWJ, Schifferstein HNJ, Wittink DR (2002) Range and number-of levels effects in derived and stated measures of attribute importance. Mark Lett 13(1):41–52

Voelckner F (2006) An empirical comparison of methods for measuring consumers’ willingness to pay. Mark Lett 17:137–149

Vohs KD, Baumeister RF, Schmeichel BJ, Twenge JM, Nelson NM, Tice DM (2008) Making choices impairs subsequent self-control: a limited resource account of decision-making, self-regulation, and active initiative. J Pers Social Psych 94:883–898

Wang X, Camm JD, Curry DJ (2009) A branch-and-price approach to the share-of-choice product line design problem. Manag Sci 55(19):1718–1728

Wathieu L, Bertini M (2007) Price as a stimulus to think: the case for willful overpricing. Mark Sci 26(1):118–129

Weber EU, Johnson EJ (2009) Mindful judgment and decision making. Annual Rev Psych 60:53–85

Weber EU, Johnson EJ, Milch KF, Chang H, Brodscholl JC, Goldstein DG (2007) Asymmetric discounting in intertemporal choice—a query-theory account. Psych Sci 18:516–523

Wedel M, Kamakura W (2000) Market segmentation: conceptual and methodological foundations, 2nd edn. Kluwer, Norwell

Wittink DR, Cattin P (1989) Commercial use of conjoint analysis: an update. J Mark 53(3):91–96

Wittink DR, Keil SK (2003) Continuous conjoint analysis. In: Gustafsson A, Herrmann A, Huber F (eds) Conjoint Measurement: methods and applications, 3rd edn. Springer, New York

Yang S, Allenby GM (2003) Modeling interdependent consumer preferences. J Mark Res 40(3):282–294

Yee M, Dahan E, Hauser JR, Orlin JB (2008) Greedoid-based noncompensatory inference. Mark Sci 26(4):532–549

Yu J, Goos P, Vandebroek M (2011) Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity. Internat J Res in Mark 28:378–388

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Naresh K. Malhotra

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Agarwal, J., DeSarbo, W.S., Malhotra, N. et al. An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research. Cust. Need. and Solut. 2 , 19–40 (2015). https://doi.org/10.1007/s40547-014-0029-5

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What Is Conjoint Analysis?

Table of Contents

When unveiling a new product or service, you need to know what your customers will value most about it. Products and services incorporate so many features these days that it can be challenging to ascertain what exactly draws customers to your offering. To better grasp customers’ wants and needs, many business leaders conduct a conjoint analysis.

Conjoint analysis is a statistical analysis and marketing research technique to measure what consumers value most about your products and services. For example, a TV manufacturer would want to know if customers value picture or sound quality more, or if they value low price more than picture quality. Conjoint analysis helps put a value on each feature, allowing you to tailor your products and services to what most consumers are seeking.

Let’s explore the conjoint analysis process – including how to conduct a conjoint analysis and how it benefits a business – and detail some conjoint analysis examples.

What is conjoint analysis?

Conjoint analysis is a tool to help you make business decisions . In an article for the Pragmatic Institute, Brett Jarvis, former global director of product management for Oracle’s Advanced Customer Services, said conjoint analysis is essentially about features and trade-offs.

“Conjoint analysis is a set of market research techniques that measures the value the market places on each feature of your product and predicts the value of any combination of features,” he wrote.

With conjoint analysis, Jarvis said, businesses ask questions of their consumers that force them to make trade-offs between features to determine what goes through their heads when deciding which products to buy. It also allows companies to perform a market analysis to simulate how the market reacts to various feature trade-offs they’re considering.

According to marketing research and analytics firm Optimization Group, conjoint analysis is based on the principle that you can better measure the relative values of a product’s or service’s features when you consider them jointly instead of in isolation.

“In business, it’s important to understand how markets value different elements of your products and services,” according to Optimization Group guidance . “Identifying these elements of higher value will enable you to optimize product development and adjust your pricing structure around the customers’ willingness to pay for specific elements.”

In addition to determining which features consumers value most about a product or service, the best conjoint analysis processes help businesses predict consumer preferences on other items they currently offer or plan to release in the future.

“One of the most important strengths of conjoint analysis is the ability to develop market simulation models that can predict consumer behavior to product changes,” according to research firm QuestionPro . “With conjoint analysis, changes in markets or products can be incorporated into the simulation to predict how consumers would react to changes.”

Conducting conjoint analysis

When conducting a conjoint analysis, you’ll determine the features you want to examine, figure out which customers to survey, and determine how to reach participants, such as by mail, over the phone, or online.

Then, place a value or ranking on each possible feature and conduct a business survey with the selected consumers, asking about the features and feature combinations they like best. The survey presents consumers with various combinations of all possibilities and asks them to rank each combination based on their preferences. Once consumers return the surveys, analyze the results to determine the optimal feature set for your needs.

Various services and software can help you set up and properly evaluate conjoint analysis data. Software solutions can help you write survey questions, set up feature combinations, and run statistical analyses on the data so you can understand the results. Popular vendors for conjoint analysis software include Sawtooth Software, Survey Analytics, Qualtrics and XLSTAT.

Benefits of using a conjoint analysis

Conjoint analysis can benefit a company in numerous ways. Businesses need to know their customers as well as possible to support them with products and services. Through conjoint analysis, you can measure actual and perceived preferences to solidify your place in the market.

Conjoint analysis also allows you to divide your target market data into smaller chunks, segmenting customers based on survey results. This makes it easy to connect to your target customers with custom marketing campaigns that yield better results.

Conjoint analysis examples

To better understand the use of conjoint analysis in business, it’s helpful to study some practical examples.

A simple example by Optimization Group centers on how consumers choose a restaurant for dinner. In this example, the features studied were distance to the restaurant, relative prices and the restaurant’s atmosphere. According to Optimization Group, diners make their decision by subconsciously weighing the different factors and choosing the restaurant that best meets their needs. In this example, the first restaurant is close to the diner and inexpensive but offers a subpar atmosphere. The second restaurant may be farther away and more expensive, but it has an excellent atmosphere.

“If you chose Restaurant No. 2, the atmosphere element obviously carried more weight in your eyes than the other two elements,” Optimization Group wrote.

For restaurants, this information is critical in determining how to design their spaces, what prices to charge, and where ideal locations are.

You can find several other examples online:

• MIT’s Sloan School of Management
• Sawtooth Software
• Pragmatic Institute

How businesses use conjoint analysis

Crucial factors that conjoint analysis can help determine include product or service pricing, marketing direction, and research and development.

• Pricing: According to an article from Harvard Business School Online , you can use conjoint analysis to gauge how much your customers are willing to pay for your products or services. Through the analysis, you can ask users to compare different features and how they value each one. You can then set new and accurate prices based on that evaluation.
• Marketing: When the analysis shows what customers value most, you can create advertisements and marketing campaigns that target those features. Alternatively, if some features don’t resonate with customers, you now know to avoid marketing those features and can even change your products.
• Research and development: You can use your analysis to see if there is enough market for new features or even a new product type. These findings show what you should target in your research and development process.

Sean Peek contributed to the writing and research in this article.

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Conjoint Analysis: a comprehensive practical guide

Appinio Research · 20.10.2022 · 12min read

Product development and market establishment pose significant challenges for many companies.

During the development process, the central questions are: What features do customers expect, and which ones are the most important?

Unfortunately, approximately 95% of new product launches fail because they do not meet customers' requirements, expectations, or needs.

Therefore, it's crucial to conduct research to answer these questions and avoid potential failures.

Fortunately, these questions can be answered during the development phase with the help of a conjoint analysis.

In this article we'll explore how you can leverage the power of the conjoint analysis enabling the prediction of potential consumers' behavior in advance by presenting realistic purchase scenarios to identify gaps in existing product competition.

What is the conjoint analysis?

The term conjoint comes from a combination of "considered" and "jointly," which also defines the conjoint analysis. It involves considering various product features (attributes) together and weighing them against other variants.

The Conjoint analysis originated in psychology and was developed by Robert Luce and John Tukey in 1964.

Since then, it has primarily been used in market research and product development to determine what attributes consumers want and perceive as particularly important during the development stage.

Attributes can include functions, designs, or features such as weight, size, and price. However, because consumers tend to want as many attributes as possible for the lowest cost, conjoint analysis takes a different approach from methods such as the MaxDiff Method .

The distinctive feature of the conjoint method is the combination of different attributes instead of independent comparison.

This makes it useful for high-priced products like automobiles, hardware such as laptops or smartphones, luxury goods, as well as everyday products or during the conception phase.

The concept of the analysis is simple.

Consumers are shown different products that differ in the combination of features.

This creates a realistic experience that closely mimics an everyday purchase decision.

For example, in a conjoint analysis to determine consumer preferences for types of chocolate, the filling attribute might be divided into levels such as vanilla cream, strawberries & cream, and kiwi ganache.

In this sample conjoint analysis, the aim is to determine which types of chocolate  consumers prefer and what price they are willing to pay for each type.

The respective attributes are leveled, i.e. they are displayed in a certain form. For the chocolate  example the filling attribute is divided into the levels vanilla cream, strawberries & cream and, kiwi ganache.

Using this approach, a ranking can be created that shows which attributes are most important and which characteristics are most attractive.

This evaluation can then be used to decide on the most appealing and profitable combination for both consumers and the company.

What is the difference between the conjoint method and Discrete Choice Model?

While there are some similarities between the Conjoint analysis and the Discrete Choice Model (DCM), there are also some notable differences.

Both models are preference-structured and designed to uncover the factors that influence consumption choices .

However, the key difference lies in how respondents view the product profiles and their attributes.

In a Conjoint analysis , respondents view the product profiles in smaller groups, while in a DCM , they see all the products simultaneously.

This makes the DCM a bit more realistic in predicting buyer behavior than the Conjoint analysis.

However, it can also be overwhelming for respondents as they are presented with a large number of options.

One of the advantages of the Conjoint analysis is that it provides more information about the attributes' relativity and importance to each other, as well as their contribution to the final buying decision. This is not possible with the DCM.

Moreover, the Conjoint analysis is an excellent tool for predicting behavior before the product is launched, which is less likely when using a DCM.

The Choice-Based conjoint method

The Choice-Based conjoint analysis (CBC) is the most popular form of conjoint analysis and for good reason.

Unlike other forms, CBC analysis asks consumers to make decisions between product variants and accept trade-offs , resulting in a more detailed and realistic analysis.

This approach reflects the fact that we make numerous decisions daily where we weigh different attributes against each other.

In CBC analysis, all previously defined attributes are combined evenly to create a statistically valid ranking at the end of the analysis.

Although there are other types of conjoint analysis, such as the Adaptive Choice conjoint and the Menu-Based conjoint analysis, they are not as flexible as the CBC method and cannot be used as widely.

At Appinio, we specialize in CBC analysis and can help you gain valuable insights into consumer preferences and behavior.

Use cases for Conjoint Analysis

The conjoint analysis is a versatile market research method suitable for a variety of use cases. Three common applications of conjoint analysis are:

Concept testing Conjoint analysis is useful for testing product concepts in the early stages of development. By identifying consumer preferences and potential flaws early on, resources can be saved and the risk of a failed product launch can be minimized.

Diversification and product range expansion The conjoint method is also helpful for testing new product variants, such as different sizes, flavors, or colors, and for optimizing the product range.

Price determination The conjoint analysis can be used to determine the optimal price for a product or service. It can be used as a stand-alone method or in combination with other price analysis techniques like Van Westendorp price analysis . By testing different concepts for their willingness to pay, businesses can make informed pricing decisions.

Conjoint Analysis' best practices

When conducting a conjoint analysis, it is important to follow best practices in order to ensure accurate results.

Here are some tips to keep in mind:

• Use short and concise descriptions of product features to avoid misunderstandings that could distort the analysis.
• Use pictures to help respondents distinguish between different variants and imagine the products being tested.
• Use descriptive comparisons for attributes rather than abstract levels such as "light" or "heavy". Concrete comparisons, such as "as heavy as a similar product," are more appropriate.

To make implementation of these tips easier, consider using the Appinio Conjoint Analysis Tool. This tool provides the necessary setting options for a successful conjoint analysis.

Book a demo and our experts will support all your research needs.

Get a free demo and see the Appinio platform in action!

Setting up a conjoint analysis (with Appinio)

Conducting a Conjoint analysis with Appinio couldn't be easier.

Step 1: Get the survey ready

Register on the Appinio platform .

Define the 3-4 most important product features (e.g. price, design) to be tested.

Contact one of our market research experts. They will guide you with formulating the definition of the product features right up until your survey goes live.

Step 2: Send your survey live

• Our research consultants will do a final check before your survey goes live.
• See the answers coming in! Our panel responds as soon as the survey is live.

Step 3: Analyze your data

• Go to the Appinio interactive dashboard and start analyzing the data you collected.
• The results of the conjoint survey are calculated and visualised in bar graphs and tables by our research consultants to show the utilities and importance of each factor. Accordingly, the results can be used immediately for decision-making.
• Export your results to Excel, PPT or CSV at any time.

What are the advantages and disadvantages of a conjoint analysis?

Conjoint analysis offers several advantages and disadvantages that should be considered when implementing this research method.

• Conjoint analysis can help determine which product features are necessary and which ones consumers would be willing to forgo.
• The analysis can measure subconscious decisions, thanks to the many different combinations of attributes and levels that can be included.

The research design is highly flexible and can be adapted to fit almost any product or concept.

The method is incredibly versatile, covering a wide range of studies such as price willingness , design tests , or product attributes .

Disadvantages

As with any research method, there are also potential disadvantages to consider when using conjoint analysis.

For example:

• Respondents may choose luxury variants since they are not actually spending any money and therefore have no sense of making a real purchasing decision. This can lead to a discrepancy between survey results and actual market behavior.

Conclusion for Conjoint Analysis

Conjoint analysis is a powerful market research tool that offers a multitude of advantages and can be used for a wide range of use cases, particularly in the areas of product development and marketing.

Its flexibility and ability to realistically reflect everyday purchase decisions make it an essential tool for businesses looking to develop and launch successful products.

With conjoint analysis, several combinations and variants can be tested without consumers having to choose their favorites from a list of attributes, allowing for a more accurate analysis of consumer preferences.

Overall, conjoint analysis is an effective way to make informed decisions about product development and marketing strategies, ultimately helping businesses to succeed in a competitive market.

Conjoint Analysis explained

What are the types of conjoint analysis?

What are the basic steps in a conjoint analysis?

What are the main goals of a conjoint analysis?

Is the conjoint analysis quantitative or qualitative?

What industries use conjoint analysis?

Ready for your conjoint analysis study with Appinio?

In our dashboard, you will find a sample tracking and questionnaire templates that you can customize and get the insights you need to bring your brand to the next level.

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What is conjoint analysis for market research?

What is the customer willing to pay for the product? Which product features are important for selling the product? Which features should the product development team prioritize? Which features are more important, and which are less important?

Have you ever asked yourself some of these questions and wanted to know the answer in an objective way? If so, you should consider implementing conjoint analysis into your market research.

In this article, you will learn what conjoint analysis is, how to design and execute it, and read examples of its implementation within product teams.

What is conjoint analysis?

Conjoint analysis is a statistical method often used by product managers to conduct market research and evaluate how customers value different product attributes.

For product managers, it’s important to know which attributes of the product increase the perceived value for the customers the most. This way you can focus on the most valuable features first and gain higher returns on investments in the development of the product.

Conjoint Analysis is one of the tools which can be used to gain these insights. The base assumption is that each product can be divided into different product attributes or product characteristics like product features, design elements, or price.

Consumers compare products with these attributes to find and buy a product that suits them the best. These attributes vary from product to product and are an important factor that customers use to determine the value of those products. So, these variations are used by product managers to create unique selling propositions (USPs) and find a product market fit .

With conjoint analysis a product managers can:

• Better select valuable product features for implementation
• Assess the right pricing strategy for a product
• Compare your own product with the competitors’ products
• Optimize the marketing and positioning of the product
• Find the right target customer groups and market segments

Key elements of conjoint analysis

In conjoint analysis, a product is broken down into its attributes and characteristics. The product manager identifies the attributes that are of the greatest interest for the conjoint analysis and collects the characteristics of these attributes for his product and competing products.

The attributes can include product features, design elements, prices, and brand names.

As these attributes differ between products, these differences can be used in customer surveys to identify customer preferences and gain insights for product development.

The product manager defines these differences per attribute in a set of levels like:

• Attribute — Size
• Levels — Small, medium, and large

Each product is listed in product profiles and presented to potential customers in surveys with specific questions.

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The type and style of how these surveys are built differ depending on the type of conjoint analysis you choose.

• Traditional conjoint analysis — Here Respondents rank or rate scenarios
• Choice-based conjoint analysis (CBC) — Respondents choose their most preferred scenario from a set of multiple-choice scenarios
• Discrete choice conjoint analysis (DCC) — Similar to CBC, respondents choose one preferred scenario from a limited set of options

Conjoint analysis example

Here is an example of a simple conjoint analysis comparing three different recruitment apps:

We will consider four attributes:

• User interface
• Job listings
• Resume builder

Each attribute has up to three levels:

• Average — 0
• Excellent — 2

Below is a table visualizing the three profiles of the apps:

The respondents will be asked to rank these apps in order of their preference, from most preferred to least preferred. For example, their ranking might look like this:

Another respondent’s ranking might be:

After collecting rankings from multiple respondents, the data will be analyzed to determine the utility values for each attribute level and the overall preference for each app. The results will help identify which attributes are most influential in driving app preferences and which app is most preferred overall by respondents.

The analysis of the data is a mathematical process. Analyzing conjoint survey results is complicated and prone to measurement errors. Often participants don’t know exactly why they choose one thing over the other.

Survey results can induce substantial bias in any direction and by any amount; this bias must be corrected with mathematical processes. Econometric and statistical methods are used to estimate a utility function for each attribute and level of the attribute.

These utility functions indicate the perceived value of the attribute and show how consumer preferences are prone to change when the level of the attribute changes.

How to design and execute a conjoint analysis study

To design and execute a conjoint analysis study, you must be clear about the objective of the research. Depending on the objective and the complexity of the questions, the study needs to be designed in different ways and different conjoint analysis types can be chosen.

The desired outcome could offer insights such as:

• Identifying customer preferences
• Optimizing feature sets
• Understanding pricing sensitivity

After setting the objective the product manager must:

• Define attributes and levels of each attribute — It’s a best practice to not model too many attributes per profile. Keep it between 3 to 10 attributes and 3 to 5 levels per attribute
• Design a choice set of products to provide in a survey — To not overwhelm respondents in the survey, keep the sample set small
• Design a survey questionnaire matching the preferred conjoint analysis — Based on the format, it’s necessary to use proper tooling for this step. This is especially for dynamic surveys
• Execute the survey to collect data — Try to understand the respondents’ demographics and filter out respondents who do not suit your target group right at the beginning
• Analyze the data using a proper tool — Most tools use mathematical models and methods like hierarchical Bayes estimation
• Calculate the part-worth utility value — Use a tool to understand the preference values of each attribute level
• Interpret the results with consideration of the research objective — Find out which attributes determine the preference of a profile the most
• Act on the results — Define a feature set for your product and alter the pricing model accordingly

With the results of the conjoint analysis survey and the mathematical model in the background, you can even use the model to simulate how the preference will change for a certain product when attribute levels are changed.

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Conjoint analysis case studies

Conjoint analysis is not bound to physical products. It can be used across all industries for physical products and services alike. You can also use it for different scenarios like identifying the right price or improving the product and service offering.

The following case studies illustrate how you could use conjoint analysis:

A fitness equipment manufacturer

Better mousetraps, improved phone service.

A company producing rowing machines was using conjoint analysis surveys to evaluate which features are most important for younger consumers who want to stock up on their home gyms. They asked different questions about the features of a new rowing machine model and found out that younger buyers would like to have rowing machines with the following attributes:

• Easier folding probabilities
• Touchscreens to play videos and see health metrics
• On-demand virtual coaching classes
• Silencing technology to keep noise level down
• Affordable compared to other competitors

They found out that different groups prioritize different things first. One group preferred convenience and quiet use, another prioritized high-tech interactive features, and another mainly looked at the price.

To optimize their products and services, a big pest control company used conjoint analysis to gain insights into the demands of modern customers. They asked in a survey what an improved mousetrap should look like. They figured out that:

• The trap should have an alert system synchronized with the smartphone
• The trap should send out low-frequency beeping sounds to keep mice outdoors
• There should be a mercy model which catches the mice without harming them
• There should be a subscription-based carefree service model where someone comes to your house and maintains the traps

After developing some of these insights customer satisfaction increased.

A phone answering service improved its offering by using a conjoint analysis based on the following attributes:

• On-demand, personal answering support
• Follow-up communication via text and social media messaging
• A pay-per-call model
• A company calendar management service
• An up-selling consulting service that helps customers to up-sell their products via phone

They identified easy ways to improve their services with little effort but with great value increases for their customers.

Advantages and disadvantages of conjoint analysis

Designing conjoint studies is complex. When too many product features and product profiles are chosen, respondents may often feel overwhelmed and tend to simplify the answers to questions.

The mathematical model that supports conjoint analysis is also very complex. The results and the way they’re calculated may not be easy to understand and interpret.

When conjoint analysis studies are poorly designed, they may overvalue product attributes which trigger emotional responses, and undervalue concrete features and important hard facts.

In the survey, the respondents are presented with all the attributes of a profile. In real life, the product positioning is harder and the consumers seldom have all the facts presented in this way. The conjoint analysis can therefore only be a reference and not directly put into practice.

On the other hand, conjoint analysis has numerous advantages. Above all, the fact that psychological mechanisms play a role in decision-making in conjoint analysis is an advantage. After all, emotions also play an important role in the real buying process.

In addition, conjoint analysis presents several attributes to the respondent in a combined manner, which corresponds better to reality than a survey in which individual attributes are queried.

In addition, conjoint analysis relates the various attributes to each other, which means that the most important factors for the user’s preferences can be identified.

Tools to design and execute conjoint analysis

There are plenty of tools out there that support product managers and market researchers with their conjoint analysis. The following are the most common:

• Sawtooth Software — Sawtooth Software is one of the most widely used and comprehensive tools for conjoint analysis. It offers various conjoint analysis techniques, including CBC, ACA, and MaxDiff. Sawtooth Software provides both standalone software packages (like Lighthouse Studio) for advanced users and online survey platforms (like Discover) for more straightforward studies
• SurveyMonkey — SurveyMonkey is another widely used online survey tool that supports conjoint analysis. While it may not have advanced conjoint analysis features like Sawtooth Software or Qualtrics, it can still be used for basic conjoint studies
• Conjoint.ly — Conjoint.ly is an online platform dedicated to conjoint analysis and related research methods. It offers automated conjoint analysis and simulation tools to analyze results and derive insights

Final thoughts

With the right preparation and a good selection of attributes and levels, conjoint analysis can give a product manager helpful insights into consumer needs. It can be used for pricing and competitive product analysis. At the same time, conjoint analysis can provide helpful insights into consumer behavior during the initial market research for a new product.

Due to the simplicity of the survey, there is no obstacle for the survey participants to take part in the conjoint study. Participants are only called upon to compare different profiles, which closely simulates a real purchase process. As a result, psychological mechanisms that play a role during the buying process are also included and flow into the conjoint analysis results.

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Using Conjoint Analysis in Marketing Research: Benefits, Examples and More

Conjoint analysis also is widely used for repositioning or revamping existing products/services.

Conjoint Analysis in Marketing Research

If you wanted to learn what a group of people wanted in a new product or service, you’d naturally think just to ask them. But, if you don’t ask good questions, you’ll get essentially meaningless information.

For example, people will say they want all the best features at the lowest price. Not helpful!

Conjoint Analysis questions mimic that process.

Conjoint Analysis involves a type of realistic series of survey questions (shown below) you ask respondents to learn which features they want, how important the features are, and their price sensitivity.

With conjoint analysis, we don’t directly ask people what’s important to them or how much they are willing to pay. Rather, we show them a series of realistic choice scenarios involving product features and (often) prices and learn that information by asking for each scenario which alternative they would choose.

How Does Conjoint Analysis Work?

In a conjoint analysis questionnaire, we show respondents product/service options (known as product concepts or profiles) and ask which one they would choose.

By scientifically varying (using an orthogonal design) the features and prices that make up the product options, we can observe what features are driving choice. We also can include a “None” option, so that respondents can tell us that none of the alternatives appeal to them.

The product alternatives vary in each scenario so that respondents have an opportunity to respond to all attributes and levels in the study. Conjoint analysis questions are usually included as part of a longer 10-20 minute market research survey.

Once we collect the data (typically we use 200 to 1000 respondents), our conjoint analysis software  fits a predictive statistical model to predict each person’s choices. The usual model is the hierarchical Bayes MNL regression model that leads to a set of preference scores (called Utilities ) for each attribute and level in the experiment.

Applications and Examples of Conjoint Analysis in Marketing

Product/service design using conjoint analysis.

Speakers at the Sawtooth Software conferences have discussed other examples of well-known companies using conjoint analysis for marketing research:

• Microsoft (design of peripherals and product line decisions)
• Procter & Gamble (design, messaging, and pricing for consumer packaged goods, CPG)
• NBC Universal Parks & Resorts (theme park experience design)
• Riot Games (video game design)
• Bose (product design and product line extension)

Marketing Segmentation Using Conjoint Analysis

Finding groups of people with similar needs (market segmentation) is critical to modern marketing practice. This lets you create a product line of differentiated products that appeal to unique market segments.

Segmentation algorithms find groups of people who have similar preferences within each group, yet different preferences between groups. Common algorithms for segmentation with conjoint analysis include latent class analysis and cluster/ensemble analysis.

When you develop market segments using conjoint analysis, you can identify which respondents belong to each segment. Profiling segment membership using other customer information, including attitudes, channels, demographics, and firmographics, helps marketers target successful communication and advertising strategies.

If you leverage needs-based segmentation to guide your product optimization, the offerings in your product line will be less likely to cannibalize your own products. Again, the conjoint market simulator together with the segmentation filters guide your efforts.

Packaging and Messaging Optimization Using MaxDiff

Firms like Procter & Gamble regularly use conjoint and MaxDiff for testing packaging styles, colors, graphics, and claims/messaging. You can create graphics with transparent layers that interact with our software to construct thousands of product options dynamically.

Pricing Research Using Conjoint Analysis

One of the most important aspects of marketing is charging the right price. Conjoint analysis provides a way to measure buyers’ price sensitivity without directly asking them how much they’re willing to pay.

When price is one of the attributes in a conjoint analysis survey, this lets us measure how respondents react to changes in price within the realistic context of competition. And, because other attributes are also varying in the scenarios, the respondent doesn’t see it just as a pricing question.

Learn why conjoint analysis is better than other research techniques for pricing research

Final Thoughts: Why Use Sawtooth Software

Conjoint analysis has become the premier tool in marketing research for product design, messaging, and pricing. Sawtooth Software is recognized worldwide as the long-standing experts with the most well-developed software for executing successful conjoint analysis studies.

Learn about our conjoint analysis software

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A Guide to Conjoint Analysis

Conjoint analysis definition + example.

Definition: Conjoint analysis is a research technique used to quantify how people value the individual features of a product or service. A conjoint survey question shows respondents a set of concepts, asking them to choose or rank the most appealing ones. When the results are displayed, each feature is scored, giving you actionable data. This data can help determine optimal product features, price sensitivity, and even market share.

Why Is It Important? Conjoint analysis goes beyond a standard rating question. It forces respondents to pick what product concepts they like best, helping identify what your audience truly values.

Interactive Conjoint Example Question

Conjoint analysis is used by any company wanting to do product research; in this example, a restaurant chain. If the chain wanted to release a new ice cream slot on their dessert menu, conjoint analysis would help determine optimal flavor, size, and price, like in the example conjoint survey question below.

This sample question has five total attributes, displayed over three sets, with three attributes shown per set.

If we offered a new menu item for ice cream, which of the following options would be most appealing to you? Please make one choice per set. If no options look appealing, choose "None."

This is an interactive example of choice based conjoint

When to Use Conjoint Analysis

Without conjoint analysis it would be impossible to ask about product prices along with flavor and size; a separate rating question for each flavor and size combination is needed. Conjoint analysis solves these problems with a straightforward survey question. When respondents evaluate this question, concept features are compared against one another, and a researcher can identify preferences.

Conjoint analysis is useful in two specific scenarios, marketing research and pricing analysis.

Marketing Research

Conjoint analysis is used in marketing research to identify what features of a product or service are most appealing to a customer base. This research can be conducted on existing products to improve advertising engagement or identify areas of improvement to increase sales. Conjoint analysis could also be used to conduct preliminary research for product feasibility.

A conjoint study will usually include demographic questions such as gender. A marketing executive can then segment the survey data by gender, revealing hidden insights used to bolster marketing strategy.

Pricing Research

Conjoint analysis is useful in pricing research because it forces customers to decide using trade-offs, helping to identify optimal prices for various levels. The ice cream example we use in this document has a $5 USD price with the highest utility, which is paired with a "medium" size. Without a conjoint study, it would have been logical to assume the "large" size should be sold for $5. Because of the trade-offs, the optimal size and price combination was found.

If the restaurant chain used multiple rating questions instead of conjoint, respondents would likely rate multiple flavors as good, and likely choose the lowest price. Using that method, it would be hard to gather reliable data.

How to Conduct a Conjoint Analysis Study

Often, preliminary data needs to be collected before running your conjoint study. An initial survey would include a MaxDiff or a Van Westendorp question to determine important product features or an acceptable price range. The preliminary survey acts as a baseline to reduce the number of conjoint concepts. A smaller number of concepts reduces survey fatigue and increases the quality of responses.

You also want to organize any custom data that you can be used in the survey. Suppose you want to segment your research by country (USA vs European customers). In that case, you need to make sure that internal data is valid, complete, and accessible by your team before running the conjoint study. If custom data is unavailable, you can add additional questions to the survey before the conjoint question.

With the preliminary survey data in hand and custom data organized, you can now create your conjoint analysis study. You can upload the product attributes and levels, include custom data, and you can add follow-up questions to ensure a successful project.

Conjoint Analysis Terminology

Conjoint analysis is an advanced research technique that uses a variety of unique terminology. To help you get a complete understanding, here is a list of commonly used conjoint terminology:

The high-level product features that respondents will evaluate are called attributes. Attributes are the first column in the above example question. That example has the following features: flavor, size, and price. If you studied a new car offering, you might have features such as color, make, model, MPG, and tire type. There is a limit of 20 attributes on the SurveyKing platform.

The items listed within an attribute are called levels. In the example, the "Flavor" attribute has levels of "Chocolate," "Vanilla," "Cookie Dough," and "Strawberry." When you create the conjoint survey, you define an attribute and the levels that go with each attribute. There is a limit of 15 levels on the SurveyKing platform.

Combining all your attributes and levels, which creates a hypothetical product, is called a concept. In the above example, concepts are the columns that respondents choose. Concepts are sometimes referred to as "cards" in statistical software. There is a limit of 7 concepts on the SurveyKing platform.

Also referred to as a task, a set contains multiple concepts or product offerings. Respondents will choose one concept per set and then be shown a new set of concepts. There is a limit of 20 sets on the SurveyKing platform.

Part-Worths/Utilities

This term is the most crucial in conjoint analysis. It defines how a respondent values each attribute level. When all the utilities for all respondents are analyzed, a researcher can determine an overall product value. Utilities are the output of a regression equation.

Utilities have no scale compared to other conjoint projects you run. They only matter in the context of the current question you are looking at.

Sometimes utilities are called "part-worths" or "part-worth utilities." We use the term "utility."

Types of Conjoint Analysis

Choice-based conjoint.

This is the most common form of conjoint. The example question above is a choice-based conjoint question. Respondents pick the most appealing concept for each set. Each set contains a random set of concepts that are evenly distributed. This type of conjoint best simulates buyer behavior since each set contains hypothetical products (concepts). When respondents choose a complete profile, a researcher can calculate preferences from the tradeoffs made. (e.g even though "Strawberry" isn't a preferred flavor, if the price were low enough, it would still provide consumer utility")

As with most conjoint studies, preliminary research is essential to reduce the number of attributes and levels to choose from. With fewer attributes and levels, the number of concepts is reduced, which lowers survey fatigue. A MaxDiff or ranking survey can be used to find the top four ice cream flavors.

Currently this is the only type of conjoint offered by SurveyKing.

Best/Worst Conjoint

Sometimes referred to as MaxDiff conjoint. Similar to choice-based conjoint, this method shows respondents a set of concepts. In each set, respondents are asked to pick the most/least (or best/worst) concepts. This approach is used when a product or service has features that cause both positive and negative reactions. An example could be studying how parents select daycare. The number of full-time faculty would draw a positive reaction. The percentage of fellow students that are economically disadvantaged could produce a negative reaction.

This is a future addition to the SurveyKing platform.

Adaptive Conjoint

This method is also similar to choice-based conjoint. Respondents pick the most appealing concept for each set, except with this method, the next set of concepts are not random but are tailored based on the previous answers. This method is more engaging to respondents and can help fine-tune the data.

Full-profile conjoint analysis

This method displays many concepts and asks respondents to rate each one based on the likelihood of purchase. This method is outdated and was primarily used prior to the introduction of survey tools that offer choice-based conjoint. Asking to rate lots of concepts at once is error-prone, quickly causes fatigue, and yields low-quality data.

Rating or Ranking Conjoint

Ranking and rating conjoint was the method used for full-profile conjoint analysis. As software has progressed, it is now possible to conduct rating or ranking conjoint similar to a choice-based conjoint. Respondents are shown a set of concepts and asked to rank or rate each concept. They could rank by entering a value for each concept, which sums to 100 for each set, or they could enter a number based on a scale. This method is also sometimes referred to as "Continuous Sum Conjoint".

Ranking conjoint is a future addition to the SurveyKing platform.

Menu-Based Conjoint

Menu-based conjoint is a new conjoint method. This method gives respondents the ability to pick multiple levels from a menu. For example, a car manufacturer could ask respondents to choose a base model and price, just like choice-based conjoint. But then they could also ask to check a box for each additional feature desired such as "Alloy Wheels for $1,500", "Sunroof for $1,000", or "Parking Assist for $1,500".

This method is much more advanced in terms of front-end programming and back-end statistics than choice-based conjoint. Often custom solutions need to be built for a company wishing to create this type of project.

Creating a Conjoint Survey

Any survey that contains a conjoint question is referred to as a conjoint survey. SurveyKing currently only offers choice-based conjoint. Here are the steps needed to create your own conjoint survey:

• Navigate to the "Builder" page of your survey
• Click on the "conjoint" element box, drag it into your questionnaire, or click the "Insert question" dropdown to add a conjoint question at the end of a specific page.
• To add a new attribute, click "Add attribute" within the conjoint builder. The builder will show levels for the attribute to the right of each attribute.
• Choose how many sets and concepts you want to display.
• Select any options to customize the question further.

Conjoint Survey Options

• "None" choice - This option will add one additional card, or column, per set that says "None" This option is marked by default. This setting reflects the real world, where consumers can choose not to buy a product. You should exclude this setting from projects where customers are forced to pick an option, such as a government service.
• Reset choices - With this option, respondents can start back at the beginning. The respondent will clear all answers for the question, and the first set will be displayed when the "reset" button is clicked. We recommend reserving this option for specific circumstances, as it could lead to second-guessing and low-quality data.

How Many Attributes, Levels, Concepts, & Sets are Needed?

An ideal conjoint question will have roughly 5 attributes (rows), 4 concepts per set (columns), and approximately 5 - 10 sets. This will help ensure respondents are not fatigued. A detailed breakdown is below:

• Attributes - Roughly 5 attributes with no more than 10 total levels per attribute. Having fewer levels per attribute ensures the survey will show various concepts more often.
• Concepts - Roughly 4 concepts to show each set. Too many concepts per set, and you risk respondents not making effective choices. The total amount of concepts available is calculated by multiplying the number of levels in each attribute. In the example above, we had four flavors, three sizes, and two prices. Total concepts available would be equal to 4 * 3 * 2 = 24. Ideally, this number should be no larger than 50. The more total concepts, the harder it becomes to draw meaningful conclusions.
• Total Sets - Showing no more than 10 total sets to respondents to avoid survey fatigue. Generally, 3-5 are best.

How Many Responses are Needed?

We recommend collecting at least 100 responses for each segment being researched. For example, if you wanted to research both males and females, you would want to collect 100 responses for both.

Conjoint Analysis Scoring & Results

Conjoint analysis uses regression to calculate how different attributes and levels are valued.

Because conjoint uses categorical data (a name like ice cream flavor) instead of continuous data (a number like a temperature), a particular type of regression is used called logistic regression . Just like any regression equation, the result of this regression calculates coefficients. These coefficients are referred to as "utilities".

Utility is not a standard unit of measure. It can be thought of as "happiness". If a lot of respondents choose concepts containing "Cookie Dough" and only a few choose concepts with "Vanilla.", even without doing the math, you can imagine that the coefficient for "Cookie Dough" would be higher than the coefficient for "Vanilla."

Let's say the coefficient for "Cookie Dough" is 5 and the coefficient for "Vanilla" is 1. We could interpret this as saying "A Cookie dough flavor of ice cream will add 5 units of happiness to a consumer, while vanilla would add only 1 unit of happiness." We would also factor in the utilities for serving size, and price, to come up with the product (or list of products) that would provide customers with the most value or "happiness".

To illustrate this concept, we ran the above ice cream example with 20 respondents. Below is the analysis of those responses. This analysis includes the utilities for each level in addition to the relative importance of each attribute.

Sample Survey Data - Summary Table

Walking through the analysis.

The utilities in the last column are the output of regression analysis. Next to each number is a small bar chart for visual representation.

Remember, utilities are not an actual unit of measurement and could be thought of as happiness. If we look at the above table, the "Cookie Dough" flavor has a utility of 14, and the "Vanilla" flavor has a utility value of 7. We could interpret this as "Cookie Dough has double the happiness of Vanilla."

The importance column is the weighted difference in utilities ranges for the product levels. You can see that flavor has the level with the largest difference of roughly 7. The larger the utility differences for an attribute, the more important they are to consumers. To get a significant difference, as we see with cookie dough, many respondents choose concepts with that flavor. We know the other levels are evenly distributed, meaning that cookie dough was a significant driving factor in decision-making regardless of size or price. Here's how you would calculate the importance:

Take the largest number for each level, and sum: 14.11+4.03+5.06 = 23.02

Divide each of the highest levels by this number. The calculation for flavor importance is 14.11 / 23.02 = 61%

Statistical Details

SurveyKing uses ChoiceModelR , a package in the R statistical program to compute conjoint utilities. ChoiceModelR calculates a coefficient using logistic regression with the maximum likelihood for each attribute level by each respondent. When the analysis is complete, utilities for each level are averaged. The output of our example can be found in this Excel file .

We color-coded the Excel file for each attribute level. Row 22 has an average subtotal, which the average utility for a specific level. The regression equations use effects coding to ensure each attribute in total sums to 0. Because of this, you will notice the excel file contains negative utilities. We shift each number by a constant to eliminate negatives and put the baseline to 0. The dark blue flavor columns were adjusted by 5.43 before the results being loaded into our dashboard. Having a 0 baseline makes the data easier to interpret.

Data used to populate ChoiceModelR:

• Data Matrix - See this Excel file , which is the input for the ice cream example. The first row of each card set contains the card number chosen (column G). The first card selected was 4. This is because the "none" option was selected. When the "none" is the chosen option, the highest index + 1 is the card selection. This is the input required for ChoiceModelR. Other programs use an output similar to this file . You'll see it's the same setup, except column G has a "1" if the card is selected or "0" if not selected. An additional row is added for the none column.
• R - The total number of iterations of the Markov chain Monte Carlo (MCMC chain) to be performed. Default value: 4000.
• Use - The number of iterations to be used in parameter estimation. Default value: 2000.
• Keep - The thinning parameter defining the number of random draws to save. Default value: 5.
• wgt - the choice-set weight parameter; possible values are 1 to 10. This parameter only needs to be specified if estimating a model using a share dependent variable. Default value: 1.
• xcoding - A number that specifies the way in which each attribute will be coded. We code each attribute as categorical, which is the value 0. Prices could technically be labeled as continuous, but for ease of calculations and consistency, we code all variables are categorical.

Time Spent Per Set

The time spent on each conjoint set is also included in the results. This data is useful to eliminate low-quality responses. Responses that answered a set too fast (under 2 seconds) should generally be eliminated from the results.

Analyzing Concept Profiles

A powerful benefit of conjoint analysis is quantifying how each concept would fare in the market. We can easily see the product with the most utility would be Cookie Dough, Medium, for $5 USD. But what about the top three products? Or the bottom three products? In the ice cream example, there were 24 hypothetical products. Unique to the SurveyKing platform is the ability to scroll through each concept in ranked order, to see what profiles faired the best or worst (or offer the most utility). The reporting section will automatically include the table shown below:

To get these figures from the Excel output file, you could create a table with all possible combinations, and use sumproduct to calculate to total utility. Here is an example .

Conjoint Analysis by Question Segments

Sometimes it's important to analyze different segments, such as gender. To do this, add a multiple-choice question to your survey for each segment you wish to study. In the reporting section, you can choose "Conjoint Segment Report." From here, select the appropriate question, and the report will output a data table for each answer. Using the ice cream example, you may notice "Males" prefer "Cookie Dough," while "Females" prefer "Vanilla." These are additional data points to fine-tune your marketing efforts.

Here is an interactive example of a conjoint comparison report unique to the SurveyKing platform. The first question asks for gender and the second question asks for a preferred ice cream concept. You can see males prefer "Cookie dough" with a utility of 23.06, while females prefer "Vanilla" with a utility of 25.63. Each gender segment lists flavor as the most important attribute. The report also includes a segmented ranking of concepts.

Analyzing Concept Market Share

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Sample Survey Data

Conjoint analysis tips.

• Keep descriptions simple - For both attributes and levels, keep the descriptions as short as possible. This will make picking choices easier and reduce survey fatigue.
• Images - Because of limited space, we recommend using images inside of each level sparingly. When images are used, we recommend that each image be custom-made for this project with a size no larger than 150px X 150px.
• Additional descriptions - Let's say you are researching a new phone. If you have a weight level of 7oz and 11oz, people won't be able to gauge that difference. You would want to say (ideally in the question text), "Use the iPhone 7 as a baseline weight, that weight would be considered average" Then the size product labels would be "Light," "Average," "Heavier."
• Be aware of incorrect conjoint content - There is a popular online video that explains conjoint analysis in Excel. The video uses "Dummy Variables" to compute the regression. This would be incorrect for two reasons. Excel cannot do logistic regression without any addons. Also, removing dummy variables is unnecessary if logistic regression is done correctly. The video codes a three-level attribute with 1's and 0's, which results in collinearity. Logistic regression assigns categorical data to a unique number. Like in our example, a four-level attribute would have the numbers 1, 2, 3, or 4, depending on what concepts were displayed.

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Conjoint Analysis for Market Research

Conjoint analysis is a powerful market research technique that measures how people make decisions based on certain features of a product or service. It decodes their purchasing behaviors helping you predict how your product or service will perform in the market.

Decode Consumer Behavior Using Conjoint Analysis

When making choices between products and services, every consumer is faced with trade-offs. Is high quality more important than a low price and quick delivery? Or is good service more important than the design, look or feel?

With Survey Analytics's sophisticated and easy-to-use conjoint analysis software tools, you can collect, analyze and act on information to make better decisions to improve on your products and services.

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Conjoint Research at the Workplace

Survey Analytics' conjoint analysis tools help companies of any size evaluate real choices people make for selecting a potential product. We put the power of conjoint market research in your hands.

Do More with Conjoint

Understanding precisely how markets value different elements of products and services means product development can be optimized.

With each respondent's score you will be able to define specific competitive contexts and project consumer choices before your product or service even hits the market. Survey Analytics' conjoint market simulators let you fine tune certain aspects of your product, such as pricing, to consumer's willingness to pay for a feature.

Conjoint Analysis In the Classroom

Whether you're taking an undergraduate course in basic research or you are a graduate student needing conjoint analysis software for your upcoming thesis, Survey Analytics can accommodate all types of research. Our enterprise research platform gives you the flexibility to run all your course research needs in just one tool.

Conjoint Analysis For Class Research

Survey Analytics' conjoint analysis tools are quickly becoming the go-to solution for students around the world. Our intuitive platform is the most effective way to get hands-on results. Create personal accounts, conduct authentic research and collaborate with users all with a click of the button.

Experimental Conjoint Research

Experimental conjoint research is a widely-used survey type in university classrooms. With Survey Analytics' conjoint tool, an easy user-interface lets you conduct market research surveys, randomly assign respondents to focus groups, and test hypotheses in minutes instead of the hours or weeks it use to take.

Survey Analytics' Conjoint Software Advantage

Sometimes it can challenging to decide which conjoint method is most appropriate for your particular research situation.

One Stop Solution For All Your Conjoint Analysis Needs

Our survey suite allows you to accurately measure consumer purchasing behavior, test your product to target distinct market segments, and select from numerous add-on modules for image, heat map, QR code and emoticon support all within a highly accurate market size simulator.

From expert, market leaders to novice researchers to graduate students, they all have successfully used our conjoint software across industries for various applications. Our enterprise research platform offers two different conjoint analysis options.

More about Conjoint Analysis

Conjoint analysis how to.

• Run Discrete Choice Conjoint Analysis
• Setup Discrete Choice Conjoint Survey
• Using Discrete Choice Data for Validation

Resources for Conjoint Analysis

• Conjoint Analysis Example
• Conjoint Analysis in Innovation Process
• Conjoint Analysis Software
• Conduct Pricing Research Effectively Using Conjoint Analysis
• Implementation Best Practices for Conjoint Analysis

Watch our Tutorial on Conjoint Analysis

Related Links and Resources

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• Conjoint Question  
• Conjoint Analysis Marketing  
• Self Explicated Conjoint Analysis  
• Sample Choice Based Conjoint  
• Discrete Conjoint Analysis  
• Conjoint Survey Free  
• Conjoint Software  
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• Choice Based Conjoint Analysis  
• Choicebased Conjoint Web Software  
• Conjoint Analysis Example  
• Conjoint Analysis In Excel  
• Conjoint Analysis Market Research  
• Conjoint Analysis Pricing  
• Conjoint Analysis Product Research  
• Conjoint Analysis Questionnaire  
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106 — Charting the Conjoint Frontier: Steve Cohen's Legacy in Market Research Greenbook Podcast

How did conjoint analysis revolutionize market research? In this episode of the Greenbook Podcast, host Lenny Murphy interviews Steve Cohen, founder and CEO of In4mation Insights and a notable figure in market research. Steve recounts his pioneering work in developing methodologies like choice-based conjoint and MaxDiff, detailing his career from early film forecasting models at Polaroid to groundbreaking academic contributions. He discusses innovations in market research tools, such as integrating budget constraints in choice modeling and enhancing analysis with behavioral economics concepts like regret minimization. We also explore the potential of advanced computational technologies and AI to enhance market research processes. You can reach out to Steve on LinkedIn. Many thanks to Steve for being our guest. Thanks also to our producer, Natalie Pusch; and our editor, Big Bad Audio. Mentioned in this episode: Join us at an IIEX Event! Visit greenbook.org/events to learn more about events in Asia, the Americas, and Europe. Use code PODCAST for 20% off general admission at all upcoming events.

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BUKD-M 572 Applied Marketing Research

• 3.00 credits
• Prerequisite(s) : Core 2

Previously offered as BUKD-C 572 Capturing and retaining market share rests squarely on your ability to understand customer behavior and to leverage that knowledge better than your competitors. Marketing research provides that important window into the marketplace. It allows you to understand the characteristics, needs, and behaviors of your current and prospective customers. It helps you to discover opportunities, design new products and services that create value for customers, develop packaging and advertising strategies to communicate this value, and create distribution, pricing, and promotion plans to deliver this value at the point of purchase. And it helps you predict how customers will respond, reducing the risk associated with business decisions. The M572 course will help you provide a competitive edge for your company by developing your skills in applying marketing research and data analysis techniques. It covers a variety of qualitative and quantitative research tools, including secondary research and syndicated data sources, observational research, focus groups, survey research, concept testing, conjoint analysis, simulated test marketing, and field experiments. The course highlights agile research techniques, where feedback is collected continuously and quickly, allowing your company to test, iterate, and adapt concepts in response to a changing environment, facilitating innovation and growth. The course is taught using several custom marketing research cases and datasets supplemented by contemporary readings and video recordings. You will learn to use statistical analysis, text mining, and online surveying software (e.g., Excel, IBM SPSS, SAS, Qualtrics, Ascribe CX Inspector) while working on the cases and preparing a team report and three individual assignments.

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Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

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X4 2024 Strategy & Research Showcase: Introducing the future of insights generation

At X4 ® 2024, we showcased how Qualtrics AI ® enables teams to quickly summarize, analyze, and elevate qualitative and quantitative data, transforming the way research is done and amplifying business outcomes at scale.

Customers tell us they’ve been flooded with potential AI solutions and specialist suppliers over the last year. AI is profoundly enhancing their efficiency and effectiveness, but with technology advancing so fast it’s hard to know what’s hype versus product truth. Meanwhile, tighter budgets make it more critical than ever to choose tools and suppliers wisely.

To help our customers conduct deep, powerful research, without breaking the bank, we’ve enhanced the XM ® product suites they know and trust with purpose-built, integrated machine intelligence. These features improve efficiency, erase data silos, and add new dimensions of scalability.

If your market research function is ready to go from a bolted-together mish-mash of tools, agencies, and third parties to a single, unified source of knowledge that generates insights on an unprecedented scale, then we think you’re going to love the new Strategy and Research Suite TM .

Here’s a look at what we unveiled at X4.

AI-powered, centralized data storage and semantic search

Researc Hub takes our single source of knowledge principle to the next level. It’s an AI powered repository of owned research spanning all of your total research knowledge capital, from customer feedback to brand studies to product insights.

Research Hub liberates your mothballed data and combines it with what’s current, so instead of commissioning a new study, you can find the answers you need using information you already own. It unites quantitative and qualitative research from all of your audiences to deliver maximum ROI from your research activities past and present.

Research Hub goes far beyond basic keywords. It uses Qualtrics-tailored AI to understand the intent and context of your search and deliver more meaningful, useful results. It does this across millions of data points, freeing your time and energy for the work that only human minds can do.

You can use Research Hub to collate and curate data collections that are relevant to your projects, then share them with team members. In the future, we’ll add functionality that lets you combine data from across multiple studies, yielding brand new insights.

Research Hub is live now with selected preview customers.

New ways to connect with consumers and get effortless insights, fast

Panels are now easier to integrate. Our customers can now connect to a third party online panel from within Qualtrics, choosing from a network of 200+ partners. You can create your own panel or select from pre-configured panel templates - whatever works best to pinpoint your target audience.

Each panel respondent interacts through our Panelist Portal app , which puts you in control of their experience. Your team can easily customize which studies participants see and even send targeted messages to segments of your audience.

Online panels are ideal for diving straight in and getting results. Where your panel requirements are more complex, our expert Research Services team is on-hand to support you.

A new generation of research features and AI tools

AI really takes the brakes off scalability, allowing you to process high volumes of qualitative data at high speed. Here are just a few of the ways our AI tools save you time and money. Many of these products are live right now or in preview with our customers.

Coming soon:

• Automated video summaries parse experience data from your panels and in-depth interviews and synthesize it into a clear summary of trends and insights. They identify up to five themes across your response data, and tie them to quotes within the transcript that exemplify the point in the respondent’s own words.
• Video transcripts are automatic, with up to 10 speakers identified, and they’re broken up into chapters for easy navigation. Naturally, sentiment and topic analysis are at work, showing up in the transcript with helpful highlights and color coding.
• Interview scheduling tools minimize the admin around online in-depth interviews. A survey screens potential participants, then offers them a time-slot when interviewers are free. They’ll get a link to join a Zoom call, hosted within the Qualtrics platform. This feature is now in preview with selected customers.
• Using our highlight reels builder , you can create video clip summaries that capture the essence of your unmoderated user testing data and tell a compelling story when you present your results.
• What if you could have a conversation with all of your respondents at once - and get coherent responses? With conversational qualitative analytics you can delve into your qualitative data using interactive dialogue to find patterns and trends. Like any good conversation, it’s effortless and engaging.
• Survey response clarity puts AI to work improving the quality of your survey data, helping you avoid having to re-run studies or rely on flawed data when making important business decisions.
• Adaptive survey follow-up reacts to your respondents’ answers, adaptively requesting additional information to add layers of meaning and context.
• All of your data forms part of a bigger picture, so we developed Dashboard Summarization to keep track of the most critical insights from across your data universe and deliver them when you need them. Less clicking through dashboards, more taking action.
• Why write your own report? Executive report generation automatically summarizes critical insights from your dashboards and presents them in a clear, actionable format.

The next generation of digital UX research

We’re bringing UX testing tools and capabilities together into a single, cost-effective product - one that sits on the same platform as the rest of your research. It unites UX essentials like surveys, card sorting, tree testing and video analysis, and methodologies like moderated and unmoderated user testing. And as before, these tools are elevated with AI functionality .

For example, with our unmoderated testing you can set up a task flow for users to complete, inviting them to record themselves. They will put your online prototype through its paces while generating audio and video data for you to transform into insights using AI tools.

Moderated testing with moderator and scheduling tools is currently in preview, and everything else is live right now for Qualtrics customers.

Find out how to transform data into outcomes with Qualtrics AI

Qualtrics // Experience Management

Qualtrics, the leader and creator of the experience management category, is a cloud-native software platform that empowers organizations to deliver exceptional experiences and build deep relationships with their customers and employees.

With insights from Qualtrics, organizations can identify and resolve the greatest friction points in their business, retain and engage top talent, and bring the right products and services to market. Nearly 20,000 organizations around the world use Qualtrics’ advanced AI to listen, understand, and take action. Qualtrics uses its vast universe of experience data to form the largest database of human sentiment in the world. Qualtrics is co-headquartered in Provo, Utah and Seattle.

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Out of the Centre

Savvino-storozhevsky monastery and museum.

Zvenigorod's most famous sight is the Savvino-Storozhevsky Monastery, which was founded in 1398 by the monk Savva from the Troitse-Sergieva Lavra, at the invitation and with the support of Prince Yury Dmitrievich of Zvenigorod. Savva was later canonised as St Sabbas (Savva) of Storozhev. The monastery late flourished under the reign of Tsar Alexis, who chose the monastery as his family church and often went on pilgrimage there and made lots of donations to it. Most of the monastery’s buildings date from this time. The monastery is heavily fortified with thick walls and six towers, the most impressive of which is the Krasny Tower which also serves as the eastern entrance. The monastery was closed in 1918 and only reopened in 1995. In 1998 Patriarch Alexius II took part in a service to return the relics of St Sabbas to the monastery. Today the monastery has the status of a stauropegic monastery, which is second in status to a lavra. In addition to being a working monastery, it also holds the Zvenigorod Historical, Architectural and Art Museum.

Belfry and Neighbouring Churches

Located near the main entrance is the monastery's belfry which is perhaps the calling card of the monastery due to its uniqueness. It was built in the 1650s and the St Sergius of Radonezh’s Church was opened on the middle tier in the mid-17th century, although it was originally dedicated to the Trinity. The belfry's 35-tonne Great Bladgovestny Bell fell in 1941 and was only restored and returned in 2003. Attached to the belfry is a large refectory and the Transfiguration Church, both of which were built on the orders of Tsar Alexis in the 1650s.  

To the left of the belfry is another, smaller, refectory which is attached to the Trinity Gate-Church, which was also constructed in the 1650s on the orders of Tsar Alexis who made it his own family church. The church is elaborately decorated with colourful trims and underneath the archway is a beautiful 19th century fresco.

Nativity of Virgin Mary Cathedral

The Nativity of Virgin Mary Cathedral is the oldest building in the monastery and among the oldest buildings in the Moscow Region. It was built between 1404 and 1405 during the lifetime of St Sabbas and using the funds of Prince Yury of Zvenigorod. The white-stone cathedral is a standard four-pillar design with a single golden dome. After the death of St Sabbas he was interred in the cathedral and a new altar dedicated to him was added.

Under the reign of Tsar Alexis the cathedral was decorated with frescoes by Stepan Ryazanets, some of which remain today. Tsar Alexis also presented the cathedral with a five-tier iconostasis, the top row of icons have been preserved.

Tsaritsa's Chambers

The Nativity of Virgin Mary Cathedral is located between the Tsaritsa's Chambers of the left and the Palace of Tsar Alexis on the right. The Tsaritsa's Chambers were built in the mid-17th century for the wife of Tsar Alexey - Tsaritsa Maria Ilinichna Miloskavskaya. The design of the building is influenced by the ancient Russian architectural style. Is prettier than the Tsar's chambers opposite, being red in colour with elaborately decorated window frames and entrance.

At present the Tsaritsa's Chambers houses the Zvenigorod Historical, Architectural and Art Museum. Among its displays is an accurate recreation of the interior of a noble lady's chambers including furniture, decorations and a decorated tiled oven, and an exhibition on the history of Zvenigorod and the monastery.

Palace of Tsar Alexis

The Palace of Tsar Alexis was built in the 1650s and is now one of the best surviving examples of non-religious architecture of that era. It was built especially for Tsar Alexis who often visited the monastery on religious pilgrimages. Its most striking feature is its pretty row of nine chimney spouts which resemble towers.

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