what is a value hypothesis gmetrix

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Value Hypothesis 101: A Product Manager's Guide

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Humans make assumptions every day—it’s our brain’s way of making sense of the world around us, but assumptions are only valuable if they're verifiable . That’s where a value hypothesis comes in as your starting point.

A good hypothesis goes a step beyond an assumption. It’s a verifiable and validated guess based on the value your product brings to your real-life customers. When you verify your hypothesis, you confirm that the product has real-world value, thus you have a higher chance of product success. 

What Is a Verifiable Value Hypothesis?

A value hypothesis is an educated guess about the value proposition of your product. When you verify your hypothesis , you're using evidence to prove that your assumption is correct. A hypothesis is verifiable if it does not prove false through experimentation or is shown to have rational justification through data, experiments, observation, or tests. 

The most significant benefit of verifying a hypothesis is that it helps you avoid product failure and helps you build your product to your customers’ (and potential customers’) needs. 

Verifying your assumptions is all about collecting data. Without data obtained through experiments, observations, or tests, your hypothesis is unverifiable, and you can’t be sure there will be a market need for your product. 

A Verifiable Value Hypothesis Minimizes Risk and Saves Money

When you verify your hypothesis, you’re less likely to release a product that doesn’t meet customer expectations—a waste of your company’s resources. Harvard Business School explains that verifying a business hypothesis “...allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.” 

If you verify your hypothesis upfront, you’ll lower risk and have time to work out product issues. 

UserVoice Validation makes product validation accessible to everyone. Consider using its research feature to speed up your hypothesis verification process. 

Value Hypotheses vs. Growth Hypotheses 

Your value hypothesis focuses on the value of your product to customers. This type of hypothesis can apply to a product or company and is a building block of product-market fit . 

A growth hypothesis is a guess at how your business idea may develop in the long term based on how potential customers may find your product. It’s meant for estimating business model growth rather than individual products. 

Because your value hypothesis is really the foundation for your growth hypothesis, you should focus on value hypothesis tests first and complete growth hypothesis tests to estimate business growth as a whole once you have a viable product.

4 Tips to Create and Test a Verifiable Value Hypothesis

A verifiable hypothesis needs to be based on a logical structure, customer feedback data , and objective safeguards like creating a minimum viable product. Validating your value significantly reduces risk . You can prevent wasting money, time, and resources by verifying your hypothesis in early-stage development. 

A good value hypothesis utilizes a framework (like the template below), data, and checks/balances to avoid bias. 

1. Use a Template to Structure Your Value Hypothesis 

By using a template structure, you can create an educated guess that includes the most important elements of a hypothesis—the who, what, where, when, and why. If you don’t structure your hypothesis correctly, you may only end up with a flimsy or leap-of-faith assumption that you can’t verify. 

A true hypothesis uses a few guesses about your product and organizes them so that you can verify or falsify your assumptions. Using a template to structure your hypothesis can ensure that you’re not missing the specifics.

You can’t just throw a hypothesis together and think it will answer the question of whether your product is valuable or not. If you do, you could end up with faulty data informed by bias , a skewed significance level from polling the wrong people, or only a vague idea of what your customer would actually pay for your product. 

A template will help keep your hypothesis on track by standardizing the structure of the hypothesis so that each new hypothesis always includes the specifics of your client personas, the cost of your product, and client or customer pain points. 

A value hypothesis template might look like: 

[Client] will spend [cost] to purchase and use our [title of product/service] to solve their [specific problem] OR help them overcome [specific obstacle]. 

An example of your hypothesis might look like: 

B2B startups will spend $500/mo to purchase our resource planning software to solve resource over-allocation and employee burnout.

By organizing your ideas and the important elements (who, what, where, when, and why), you can come up with a hypothesis that actually answers the question of whether your product is useful and valuable to your ideal customer. 

2. Turn Customer Feedback into Data to Support Your Hypothesis  

Once you have your hypothesis, it’s time to figure out whether it’s true—or, more accurately, prove that it’s valid. Since a hypothesis is never considered “100% proven,” it’s referred to as either valid or invalid based on the information you discover in your experiments or tests. Additionally, your results could lead to an alternative hypothesis, which is helpful in refining your core idea.

To support value hypothesis testing, you need data. To do that, you'll want to collect customer feedback . A customer feedback management tool can also make it easier for your team to access the feedback and create strategies to implement or improve customer concerns. 

If you find that potential clients are not expressing pain points that could be solved with your product or you’re not seeing an interest in the features you hope to add, you can adjust your hypothesis and absorb a lower risk. Because you didn’t invest a lot of time and money into creating the product yet, you should have more resources to put toward the product once you work out the kinks. 

On the other hand, if you find that customers are requesting features your product offers or pain points your product could solve, then you can move forward with product development, confident that your future customers will value (and spend money on) the product you’re creating. 

A customer feedback management tool like UserVoice can empower you to challenge assumptions from your colleagues (often based on anecdotal information) which find their way into team decision making . Having data to reevaluate an assumption helps with prioritization, and it confirms that you’re focusing on the right things as an organization.

3. Validate Your Product 

Since you have a clear idea of who your ideal customer is at this point and have verified their need for your product, it’s time to validate your product and decide if it’s better than your competitors’. 

At this point, simply asking your customers if they would buy your product (or spend more on your product) instead of a competitor’s isn’t enough confirmation that you should move forward, and customers may be biased or reluctant to provide critical feedback. 

Instead, create a minimum viable product (MVP). An MVP is a working, bare-bones version of the product that you can test out without risking your whole budget. Hypothesis testing with an MVP simulates the product experience for customers and, based on their actions and usage, validates that the full product will generate revenue and be successful.  

If you take the steps to first verify and then validate your hypothesis using data, your product is more likely to do well. Your focus will be on the aspect that matters most—whether your customer actually wants and would invest money in purchasing the product.

4. Use Safeguards to Remain Objective 

One of the pitfalls of believing in your product and attempting to validate it is that you’re subject to confirmation bias . Because you want your product to succeed, you may pay more attention to the answers in the collected data that affirm the value of your product and gloss over the information that may lead you to conclude that your hypothesis is actually false. Confirmation bias could easily cloud your vision or skew your metrics without you even realizing it. 

Since it’s hard to know when you’re engaging in confirmation bias, it’s good to have safeguards in place to keep you in check and aligned with the purpose of objectively evaluating your value hypothesis. 

Safeguards include sharing your findings with third-party experts or simply putting yourself in the customer’s shoes.

Third-party experts are the business version of seeking a peer review. External parties don’t stand to benefit from the outcome of your verification and validation process, so your work is verified and validated objectively. You gain the benefit of knowing whether your hypothesis is valid in the eyes of the people who aren’t stakeholders without the risk of confirmation bias. 

In addition to seeking out objective minds, look into potential counter-arguments , such as customer objections (explicit or imagined). What might your customer think about investing the time to learn how to use your product? Will they think the value is commensurate with the monetary cost of the product? 

When running an experiment on validating your hypothesis, it’s important not to elevate the importance of your beliefs over the objective data you collect. While it can be exciting to push for the validity of your idea, it can lead to false assumptions and the permission of weak evidence. 

Validation Is the Key to Product Success

With your new value hypothesis in hand, you can confidently move forward, knowing that there’s a true need, desire, and market for your product.

Because you’ve verified and validated your guesses, there’s less of a chance that you’re wrong about the value of your product, and there are fewer financial and resource risks for your company. With this strong foundation and the new information you’ve uncovered about your customers, you can add even more value to your product or use it to make more products that fit the market and user needs. 

If you think customer feedback management software would be useful in your hypothesis validation process, consider opting into our free trial to see how UserVoice can help.

Heather Tipton

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Blog » Value Hypothesis & Growth Hypothesis: lean startup validation

Value Hypothesis & Growth Hypothesis: lean startup validation

Posted on September 16, 2021 |

You’ve come up with a fantastic idea for a startup and you need to discuss the hypothesis and its value? But you’re not sure if it’s a viable one or not. What do you do next? It’s essential to get your ideas right before you start developing them. 95% of new products fail in their first year of launch. Or to put it another way, only one in twenty product ideas succeed. In this article, we’ll be taking a look at why it’s so important to validate your startup idea before you start spending a lot of time and money developing it. And that’s where the Lean Startup Validation process gets into, alongside the growth hypothesis and value hypothesis. We’ll also be looking at the questions that you need to ask.

Table of contents

The lean startup validation methodology, the benefits of validating your startup idea, the value hypothesis, the growth hypothesis, recommendations and questions for creating and running a good hypothesis, in conclusion – take the time to validate your product.

What does it mean to validate a lean startup?

Validating your lean startup idea may sound like a complicated process, but it’s a lot simpler than you may think. It may be the case that you were already planning on carrying out some of the work.

Essentially, validating your startup when you check your idea to see if it solves a problem that your prospective customers have. You can do this by creating hypotheses and then carrying out research to see if these hypotheses are true or false. 

The best startups have always been about finding a gap in the market and offering a product or service that solves the problem. For example, take Airbnb . Before Airbnb launched, people only had the option of staying in hotels. Airbnb opened up the hospitality industry, offering cheaper accommodation to people who could not afford to stay inexpensive hotels. 

“Don’t be in a rush to get big. Be in a rush to have a great product” – Eric Ries

Validation is a crucial part of the lean startup methodology, which was devised by entrepreneur Eric Ries. The lean startup methodology is all about optimizing the amount of time that is needed to ensure a product or service is viable. 

Lean Startup Validation is a critical part of the lean startup process as it helps make sure that an idea will be successful before time is spent developing the final product.

As an example of a failed idea where more validation could have helped, take Google Glass . It sounded like a good idea on paper, but the technology failed spectacularly. Customer research would have shown that $1,500 was too much money, that people were worried about health and safety, and most importantly… there was no apparent benefit to the product.

Find out more about lean startup methodology on our blog

How to create a mobile app using lean startup methodology

The key benefit of validating your lean startup idea is to make sure that the idea you have is a viable one before you start using resources to build and promote it. 

There are other less obvious benefits too:

• It can help you fine-tune your idea. So, it may be the case that you wanted your idea to go in a particular direction, but user research shows that pivoting may be the best thing to do
• It can help you get funding. Investors may be more likely to invest in your startup idea if you have evidence that your idea is a viable one

The value hypothesis and the growth hypothesis – are two ways to validate your idea

“To grow a successful business, validate your idea with customers” – Chad Boyda

In Eric Rie’s book ‘ The Lean Startup’ , he identifies two different types of hypotheses that entrepreneurs can use to validate their startup idea – the growth hypothesis and the value hypothesis. 

Let’s look at the two different ideas, how they compare, and how you can use them to see if your startup idea could work.

The value hypothesis tests whether your product or service provides customers with enough value and most importantly, whether they are prepared to pay for this value.

For example, let’s say that you want to develop a mobile app to help dog owners find people to help walk their dogs while they are at work. Before you start spending serious time and money developing the app, you’ll want to see if it is something of interest to your target audience. 

Your value hypothesis could say, “we believe that 60% of dog owners aged between 30 and 40 would be willing to pay upwards of €10 a month for this service.”

You then find dog owners in this age range and ask them the question. You’re pleased to see that 75% say that they would be willing to pay this amount! Your hypothesis has worked! This means that you should focus your app and your advertising on this target audience. 

If the data comes back and says your prospective target audience isn’t willing to pay, then it means you have to rethink and reframe your app before running another hypothesis. For example, you may want to focus on another demographic, or look at reducing the price of the subscription.

Shoe retailer Zappos used a value hypothesis when starting out. Founder Nick Swinmurn went to local shoe stores, taking photos of the shoes and posting them on the Zappos website. Then, if customers bought the shoes, he’d buy them from the store and send them out to them. This allowed him to see if there was interest in his website, without having to spend lots of money on stock.

The growth hypothesis tests how your customers will find your product or service and shows how your potential product could grow over the years.

Let’s go back to the dog-walking app we talked about earlier. You think that 80% of app downloads will come from word-of-mouth recommendations.

You create a minimal viable product ( MVP for short ) – this is a basic version of your app that may not contain all of the features just yet. So, you then upload it to the app stores and wait for people to start downloading it. When you have a baseline of customers, you send them an email asking them how they heard of your app.

When the feedback comes back, it shows that only 30% of downloads have come from word-of-mouth recommendations. This means that your growth hypothesis has not been successful in this scenario. 

Does this mean that your idea is a bad one? Not necessarily. It just means that you may have to look at other ways of promoting your app. If you are relying on word-of-mouth recommendations to advertise it, then it could potentially fail.

Dropbox used growth hypotheses to its advantage when creating its software. The file-storage company constantly tweaked its website, running A/B tests to see which features and changes were most popular with customers, using them in the final product.

Like any good science experiment, there are things that you need to bear in mind when running your hypotheses. Here are our recommendations:

• You may be wondering which type of hypothesis you should carry out first – a growth hypothesis or a value hypothesis. Eric Ries recommends carrying out a value hypothesis first, as it makes sense to see if there is interest before seeing how many people are interested. However, the precise order may depend on the type of product or service you want to sell;
• You will probably need to run multiple hypotheses to validate your product or service. If you do this, be sure to only test one hypothesis at a time. If you end up testing multiple ones in one go, you may not be sure which hypothesis has had which result;
• Test your most critical assumption first – this is one that you are most worried about, and could affect your idea the most. It may be that solving this issue makes your product or service a viable one;
• Specific – is your hypothesis simple? If it’s jumbled or confusing, you’re not going to get the best results from it. If you’re struggling to put together a clear hypothesis, it’s probably a sign to go back to the drawing board.
• Measurable – can your hypothesis be measured? You’ll want to get tangible results so you can check if the changes you have made have worked.
• Achievable – is your hypothesis attainable? If not, you may want to break it down into smaller goals.
• Relevant – will your hypothesis prove the validity of your product or service? 
• Timely – can your hypothesis be measured in a set amount of time? You don’t want a goal that will take years to monitor and measure!
• Be as critical as possible. If you have created an idea, it is only natural that you want it to succeed. However, being objective rather than subjective will help your startup most in the long term;
• When you are carrying out customer research, use as vast a pool of people as time and money will allow. This will result in more accurate data. The great news is that you can use social media and other networking sites to reach out to potential customers and ask them their opinions;
• When carrying out customer research, be sure to ask the questions that matter. Bear in mind that liking your product or service isn’t the same as buying it. If a customer is enthusiastic about your idea, be sure to ask follow-on questions about why they like it, or if they would be willing to spend money on it. Otherwise, your data may end up being useless;
• While it is essential to have as many relevant hypotheses as possible, be careful not to have too many.  While it may sound like a good idea to try out lots of different ideas, it can actually be counter-productive. As Eric Ries said:

“Don’t bog new teams down with too much information about falsifiable hypotheses. Because if we load our teams up with too much theory, they can easily get stuck in analysis paralysis. I’ve worked with teams that have come up with hundreds of leap-of-faith assumptions. They listed so many assumptions that were so detailed and complicated that they couldn’t decide what to do next. They were paralyzed by the just sheer quantity of the list.”

“We must learn what customers really want, not what they say they want or what we think they should want.” – Eric Ries

According to CB Insights , the number one reason why startups fail is that there is no demand for the product. Many entrepreneurs have gone ahead and launched a product that they think people want, only to find that there is no market at all.

Lean Startup Validation is essential in helping your business idea to succeed. While it may seem like extra work, the additional work you do in the beginning will be of a critical advantage later down the line.

Still not 100% convinced? Take HubSpot . Before HubSpot launched its sales and marketing services, it started off as a blog. Co-founders Dharmesh Shah and Brian Halligan used this blog to validate their ideas and see what their visitors wanted. This helped them confirm that their concept was on the right lines and meant they could launch a product that people actually wanted to use.

Validating a startup idea before development is crucial because it ensures that the idea is viable and addresses a real problem that customers have. With a high failure rate of new products, validation helps avoid wasting time and resources on ideas that might not succeed.

The value hypothesis tests whether customers find enough value in a product or service to pay for it. The growth hypothesis examines how customers will discover and adopt the product over time. Both hypotheses are essential for validating the viability of a startup idea.

Eric Ries recommends starting with a value hypothesis before a growth hypothesis. Validating whether the idea provides value is crucial before considering how to promote and grow it.

When creating and running a hypothesis, consider the following: 1. Focus on testing one hypothesis at a time. 2. Test your most critical assumptions first. 3. Ensure your hypothesis follows SMART goals (Specific, Measurable, Achievable, Relevant, Timely). 4. Use a wide pool of potential customers for accurate data. 5. Ask relevant and probing questions during customer research. 6. Avoid overwhelming your team with excessive hypotheses.

Validating your product idea before development helps you avoid the top reason for startup failure—lack of demand for the product. By confirming that there is a market need and interest in your idea, you increase the chances of building a successful product.

Lean Startup Validation helps entrepreneurs avoid the mistake of launching a product that doesn’t address a genuine need. By gathering evidence and feedback early, you can make informed decisions about pivoting or refining your idea before investing significant time and resources.

Certainly. Suppose you’re developing a mobile app for dog owners to find dog-walking services. Your value hypothesis could be: “We believe that 60% of dog owners aged between 30 and 40 would be willing to pay upwards of €10 a month for this service.” You then validate this hypothesis by surveying dog owners in that age range and analyzing their responses.

The growth hypothesis examines how customers will discover and adopt your product. If, for example, you expect 80% of app downloads to come from word-of-mouth recommendations, but feedback shows only 30% are from this source, you may need to reevaluate your promotion strategy.

Yes, Lean Startup Validation can be applied to startups across various industries. Whether you’re offering a product or service, the process of testing hypotheses and gathering evidence applies universally to ensure the viability of your idea.

To gather accurate data, focus on reaching a diverse pool of potential customers through various channels, including social media and networking sites. Ask relevant questions about their preferences, willingness to pay, and potential pain points related to your idea

Being critical and objective during validation helps you avoid confirmation bias and wishful thinking. Objectivity allows you to assess whether your idea truly addresses a problem and resonates with customers, ensuring that your startup’s foundation is built on solid evidence.

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Template: Determining Your Value Hypothesis

• CEO at The Long-Term Stock Exchange

About this template

Early on in the development of a product or service , you will need to craft a value hypothesis . This hypothesis will rest at the heart of your startup’s viability and will enable you to test the relative worth of your product or service to your target consumer. Below is a sequence for formulating a verifiable value hypothesis , along with guidance on how to develop each component.

The structure of a value hypothesis

Before diving into the nuts and bolts, let’s look at the general structure of a Value Hypothesis :

This type of consumer ( target customer/client ) will pay ( cost ) to buy/use/etc. ( product or service ) to help with ( problem ).

At its root, a value hypothesis takes a handful of assumptions about your product/service and structures them so as to be verified or rejected .

Here are some examples:

At-home shoppers will pay an extra $15 service fee to have DeliveryDrones deliver their package within hours of ordering online.

Working pet owners will pay a $249 yearly subscription fee to PetsITr to monitor and interact with their pets via a two-way video stream.

Busy health-conscious adults will pay $8 to MegaMeal for a meal replacement drink containing a full day’s serving of fruits, vegetables and protein.

Defining your target customer/client

A target customer is likely a concept you’re already well acquainted with. They are the segment of potential consumers you intend to market your product to .

Some questions to consider as you determine your target customer/client :

What’s your target customer/client’s age/gender/ethnicity?

What is your target customer/client’s geographical location, what’s your target customer/client’s marital status, what’s your target customer/client’s occupation/economic status, what’s your target customer/client’s education level, what are some of your target customer/client’s interests/hobbies, what are some of your target customer/client’s values, defining the problem you’re trying to solve.

“The Problem” was likely the nucleus of the big idea for your startup. It’s the question that is answered with the existence of your product or service . It can be viewed through a few different lenses: Market Need, Market Opportunity, Core Product Features, etc.

Here are some questions to help you hone in on your the problem your product or service is attempting to address :

What are some fundamental jobs your target customer needs help doing?

What are some needs your target customer needs help meeting, what are some current solutions your target customer is utilizing to do those jobs/meet those needs, how effective are those current solutions performing for your target customer, how does your product/service improve upon those solutions, determining the cost of your product or service.

Now it’s time to make the leap-of-faith assumption of how much your target customer will value your product/service . This form’s assumption will be the key metric of your hypothesis. Here are some questions to consider as you determine the cost of your product/service .

How large is your target demographic?

Can your target customer/client pay for your product/service, how much will your target customer/client pay for your product/service, how much does your competitor charge for a similar product/service, how much do you need to charge to turn a profit, putting it all together, who is your target audience, what is the problem your product/service seeks to address, what cost will your target audience pay for your product/service, write out your value hypothesis.

Using the following structure:

Write out your value hypothesis:

View the rest of 'the lean startup' templates, quarterly business review (qbr) memo template, brie wolfson, weekly standup template, allison daley, product roadmap template, clare price, prd template, katherine kampf, andrew logemann, template: the build/measure/learn loop worksheet.

Team and Org Chart

Guide: asynchronous communication, darren murph, go-to-market plan (gtm plan) template, yelena kozlova.

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What is a Value Hypothesis?

Ruben Buijs

Founder & Digital Consultant

Written on Aug 10, 2023

Product Management

A value hypothesis is a fundamental concept in Saas product management that helps teams determine and validate the value their product brings to customers. It involves making assumptions about the value customers will derive from using the product and then testing and validating those assumptions through experimentation and customer feedback .

Let's consider an example to illustrate the concept of a value hypothesis. Imagine a team building a project management software. Their value hypothesis might be that by using their software, teams will be able to increase their productivity and efficiency by 30%. They would then design experiments and gather feedback from users to validate this hypothesis. If the feedback and data confirm the hypothesis, the team can be confident that their product delivers the promised value.

The value hypothesis is crucial for Saas product management for several reasons. Firstly, it helps product teams align their efforts and focus on building features and functionalities that truly deliver value to customers. By clearly defining the value proposition , teams can avoid wasting time and resources on unnecessary or low-impact features.

Secondly, the value hypothesis serves as a guide for prioritizing product development efforts. It helps the team identify and prioritize the most critical functionalities that need to be built to fulfill the value proposition. This ensures that the product meets customer expectations and provides a competitive advantage.

Lastly, the value hypothesis guides product marketing and sales efforts. It helps create compelling messaging and positioning that highlights the unique value the product brings to the market. It also assists in setting pricing strategies based on the perceived value of the product.

• How to Use Value Hypothesis

Define the value proposition : Clearly articulate the value your product aims to deliver to customers. This could be increased productivity, cost savings, improved collaboration, or any other benefit that aligns with your target audience's needs.

Make assumptions: Formulate specific assumptions about the value customers will derive from using your product. For example, you might assume that your product will help customers save 20 hours per week or reduce operational costs by 15%.

Design experiments: Create experiments to test your assumptions. These experiments could include user interviews, surveys, usability testing , or A/B testing . The goal is to collect data and feedback that either validates or disproves your value hypothesis.

Analyze and iterate: Analyze the data and feedback gathered from the experiments and iterate on your value hypothesis if necessary. If the data supports your assumptions, you can proceed with confidence. If not, adjust your hypothesis and repeat the experimentation process until you have a validated value proposition.

• Useful Tips

Involve customers early on: Engage with potential customers during the value hypothesis formulation stage to gain insights and validate assumptions before investing heavily in product development.

Test one assumption at a time: To ensure clarity and focus, test each assumption separately. This allows you to accurately identify which specific value proposition resonates most with your target audience.

Continuously gather feedback: Regularly collect feedback from customers to validate and refine your value hypothesis throughout the product lifecycle . This helps you adapt to changing market needs and stay ahead of the competition.

• Related Terms
• Product-Market Fit
• Minimum Viable Product (MVP)
• Customer Validation
• Value Proposition
• User Interviews
• Usability Testing
• A/B Testing
• Competitive Advantage

What is a value hypothesis?

Why is a value hypothesis important, how do you create a value hypothesis, what should a value hypothesis include, can a value hypothesis change over time, how can you validate a value hypothesis, what happens if a value hypothesis is proven wrong, can multiple value hypotheses coexist, should a value hypothesis be tested before development, how often should a value hypothesis be evaluated.

Ruben is the founder of ProductLift. I employ a decade of consulting experience from Ernst & Young to maximize clients' ROI on new Tech developments. I now help companies build better products

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Understanding Lean Startup Validation: What Is a Value Hypothesis?

Understanding Lean Startup Principles in Relation to Value Hypothesis

When exploring the Lean Startup methodology and its relationship with the value hypothesis , it's essential to understand how these principles intertwine. By integrating lean principles with the value hypothesis , entrepreneurs can effectively validate their startup ideas and drive sustainable business development.

Lean Startup Methodology and Value Hypothesis

Integrating lean principles with value hypothesis.

Incorporating the core tenets of lean startup methodology, such as rapid iteration and validated learning, into the formulation of a value hypothesis is crucial for refining and validating a startup idea.

Lean Startup Benefits for Value Hypothesis

The lean startup approach offers invaluable benefits for shaping a robust value hypothesis, including efficient resource allocation, risk mitigation, and accelerated product-market fit.

Aligning Lean Principles with Value Hypothesis

Aligning lean principles with the value hypothesis ensures that startups remain customer-centric, adaptable, and responsive to market dynamics.

Importance of Lean Startup Validation for Value Hypothesis

Reducing business risks through value hypothesis.

Validating a value hypothesis through lean principles minimizes the inherent risks associated with untested assumptions, thereby safeguarding business resources.

Enhancing Product Market Fit with Value Hypothesis

By leveraging lean startup validation processes, entrepreneurs can enhance their product-market fit by aligning their offerings closely with customer needs and preferences.

Accelerating Business Growth with Value Hypothesis

A well-validated value hypothesis paves the way for accelerated business growth by fostering innovation, customer satisfaction, and competitive differentiation.

Lean Startup Validation process: The integration of lean principles into the validation process is instrumental in ensuring that startups develop viable solutions that resonate with their target audience.

Understanding the Value Hypothesis

In the realm of lean startup methodology , a value hypothesis plays a pivotal role in shaping the trajectory of a new venture. It serves as a foundational premise that guides entrepreneurs in developing products or services that resonate with their target audience, thereby increasing the likelihood of success.

Defining a Value Hypothesis

Components of a value hypothesis.

A value hypothesis comprises several essential components, including the identification of customer pain points, an articulation of how the proposed solution addresses these pain points, and an estimation of how customers will perceive and adopt the solution.

Importance of a Clear Value Hypothesis

Crafting a clear and concise value hypothesis is crucial for aligning internal teams, investors, and stakeholders around a common understanding of the problem being solved and the proposed solution. It provides clarity and direction for all subsequent product development efforts.

Crafting an Effective Value Hypothesis

An effective value hypothesis is not only specific but also measurable. It should articulate clear success criteria that can be objectively evaluated to determine whether the proposed solution has indeed created value for its intended users.

The Role of a Value Hypothesis in Lean Startup

Aligning with customer needs.

The value hypothesis makes explicit assumptions about what customers truly value and how those values can be addressed through innovative solutions. This alignment ensures that startups remain focused on delivering tangible benefits to their customers.

Iterative Product Development

Embracing a value hypothesis within lean startup principles fosters iterative product development, where each iteration is designed to test and validate specific aspects of the value proposition. This iterative approach allows for continuous refinement based on real-time feedback from customers.

Behind the Scenes

Historical Examples:

Eric Ries' first company, Catalyst Recruiting , failed because he and his colleagues did not understand the wants of their target customers, focusing too much time and energy on the initial product launch. Ries later published “ The Lean Startup ” book in 2011, aiming to improve upon what had been going on with startups and tech companies. He was inspired by his own company's failure due to not understanding customer needs.

Expert Testimony:

"Lean startup emphasizes customer feedback over intuition and flexibility over planning."

"Testing and validating your hypotheses is an essential part of startup development as it helps you reduce uncertainty, avoid failure, and create value for your customers."

Importance of a Value Hypothesis

In the realm of startup development, a value hypothesis serves as the compass guiding entrepreneurs toward creating products or services that resonate with their target audience. Understanding the significance of a value hypothesis entails embracing a customer-centric approach and leveraging market differentiation strategies.

Customer-Centric Approach to Value Hypothesis

Understanding customer pain points for value hypothesis.

A fundamental aspect of a value hypothesis involves delving into the pain points experienced by customers . By comprehensively understanding these pain points, entrepreneurs can tailor their solutions to directly address the specific needs and challenges faced by their target audience.

Tailoring Products to Customer Needs with Value Hypothesis

The essence of a value hypothesis lies in its ability to steer product development efforts toward catering to the unique requirements and preferences of customers . This tailored approach ensures that the resulting offerings align closely with what customers truly value, thereby increasing the likelihood of widespread adoption and satisfaction.

Building Customer Loyalty through Value Hypothesis

Statistical data highlights that at least one-third of respondents emphasize human interaction as crucial for their loyalty, while more than half express a preference for an enjoyable online shopping experience. A well-crafted value hypothesis enables entrepreneurs to build customer loyalty by addressing these key aspects that influence consumer allegiance.

Market Differentiation and Value Hypothesis

Identifying unique value propositions.

An effective value hypothesis aids in identifying and articulating unique value propositions that set a venture apart from competitors. By pinpointing what makes their offerings distinct, entrepreneurs can effectively communicate this differentiation to potential customers, fostering brand loyalty and preference.

Creating Competitive Advantage through Value Hypothesis

The strategic formulation and validation of a robust value hypothesis empower startups to create sustainable competitive advantages within their respective markets. This advantage stems from aligning products or services with customer needs in ways that outperform existing alternatives, positioning the venture for long-term success.

Validating the Value Hypothesis

In the realm of lean startup methodology, validating the value hypothesis is a critical phase that involves leveraging various methods and tools to ensure that a product or service genuinely delivers value to the customer. This process not only reduces uncertainty but also paves the way for creating solutions that address real needs and preferences.

Research and Data Analysis for Value Hypothesis

Conducting market research for value hypothesis.

Market research serves as a foundational step in validating the value hypothesis. It involves gathering insights into consumer behavior, market trends, and competitor offerings to assess the potential reception of the proposed solution.

Analyzing User Feedback for Value Hypothesis

User feedback analysis provides invaluable qualitative data regarding how customers perceive and interact with a product or service. This analysis helps in refining the value hypothesis based on authentic user experiences and preferences.

Iterative Testing of Value Hypothesis

Prototyping and mvp testing for value hypothesis.

Prototyping and minimum viable product (MVP) testing are instrumental in validating the value hypothesis. These methods allow entrepreneurs to gather real-world feedback on early versions of their offerings, enabling iterative refinement based on user responses.

A/B Testing and Experiments for Value Hypothesis

A/B testing involves comparing different versions of a product or feature to determine which resonates best with users. By conducting experiments through A/B testing, startups can validate their value hypotheses by identifying features that drive meaningful engagement and satisfaction.

Measuring Customer Value with Value Hypothesis

Key metrics for value assessment with value hypothesis.

Key metrics, such as customer acquisition cost, lifetime value, and retention rates, provide quantifiable indicators of customer value. Measuring these metrics allows startups to gauge how well their offerings align with customer needs and expectations.

Customer Satisfaction Surveys for Value Hypothesis

Customer satisfaction surveys offer direct insights into how customers perceive the value delivered by a product or service. These surveys help in understanding areas of strength and improvement within the value proposition.

Long-Term Value Measurement with Value Hypothesis

Long-term measurement involves tracking customer satisfaction, loyalty, and advocacy over extended periods. This longitudinal assessment provides a comprehensive view of how well a product or service continues to deliver value over time.

By employing these validation methods , startups can systematically refine their value hypotheses based on empirical evidence gathered from market dynamics and user interactions.

Key Recommendations for Value Hypothesis

When formulating a value hypothesis , several key recommendations can significantly impact its effectiveness and the subsequent validation process.

Crafting a Good Hypothesis for Value Hypothesis

Clarity and specificity in value hypothesis.

A well-crafted value hypothesis should be clear, specific, and unambiguous. It must articulate the problem being addressed, the proposed solution, and the expected outcomes with precision to guide subsequent product development efforts effectively.

Testability and Measurability of Value Hypothesis

An effective value hypothesis should be testable and measurable. It should define success criteria that can be objectively evaluated, allowing startups to gather empirical evidence to validate whether their proposed solution genuinely creates value for their target audience.

Lean Startup Principles and Value Hypothesis

Embracing iterative development for value hypothesis.

Incorporating lean startup principles into the formulation of a value hypothesis involves embracing iterative development. This approach advocates rapid iteration, constant feedback loops, and validated learning to refine the value proposition based on real-time insights from users.

Customer-Centric Mindset in Value Hypothesis

A customer-centric mindset is pivotal when crafting a value hypothesis . Startups must prioritize understanding customer needs, preferences, and pain points to tailor their solutions effectively while aligning with lean principles of continuous improvement through user feedback.

Time and Resource Allocation for Value Hypothesis

Efficient resource management for value hypothesis.

Efficient resource allocation is crucial when validating a value hypothesis within the lean startup framework. Startups need to optimize resource allocation by focusing on high-impact activities that contribute to refining the value proposition based on validated learning.

Balancing Speed and Quality in Value Hypothesis

Balancing speed with quality is essential when validating a value hypothesis . While rapid iteration is encouraged within lean principles, startups must ensure that speed does not compromise the quality of insights gathered or the refinement process based on accurate data analysis.

Growth Hypothesis in Relation to Value Hypothesis

Establishing scalable sales strategies for value hypothesis.

The growth hypothesis examines how validated value propositions can impact product sales at scale. It focuses on identifying opportunities for sustainable revenue generation by leveraging a well-validated value hypothesis as an integral part of scalable sales strategies.

Repeatable Revenue Generation with Value Hypothesis

Validating a value hypothesis also involves testing its impact on repeatable revenue generation. By understanding how well the value proposition resonates with customers over time, startups can refine their offerings to ensure consistent revenue streams through sustained customer satisfaction.

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How to Screw up Everything but Still Succeed. Guide to Product-Market Fit

mins to read

Product-market fit (PMF) sounds like a very basic concept, the one you can understand just by the name. However simple it is in theory, in practice it is one of the hardest things to get as an emerging company. And just as crucial.

As a design agency , we help create MVPs (minimum viable product) for different SaaS businesses. Quality MVP is indeed important, but the product-market fit is the one thing that really makes or breaks the success of an MVP and the whole startup.

So let’s uncover what exactly it is and what it takes to achieve it.

What is product-market fit and why should you care?

The first one to define the product-market fit was Marc Andreessen. Here’s how it sounds like:

Product-market fit means being in a good market with a product that can satisfy that market.

Fair question here would be, what is a “good market”? It means having lots of potential customers who have some underserved needs and therefore are likely to use your product.

But what this definition doesn’t tell is that if you are a startup owner, you’re in the middle of a race to achieve product-market fit. Furthermore, this race doesn’t have an end date — it is an ongoing process of continuous monitoring and adaptation. Why so? Because the needs and preferences of the market never remain the same. They are evolving, and so does the fit between a product and the market.

Still, the process of achieving product-market fit doesn’t change that much. Let’s discover the essential steps required to achieve it.

6 steps to product-market fit and one pyramid

Eric Ries described one of the easiest ways of achieving product-market fit in his book The Lean Startup . It is also known as The Lean Product Process and includes just six steps:

1. Find target customer

2. Define what are their needs

3. Develop a value proposition

4. Figure out the main MVP

5. Build an MVP  

6. Test and collect feedback

Product-market fit is one of the key elements of Lean Startup theory, as well as the product-market fit pyramid. It is a great visualization of how product-market fit connects different components of the product design process.

Product-market fit pyramid

6 steps may turn into 12 or 18 or more if the initial hypothesis doesn’t prove true. After each iteration, you have to check the metrics to realize how close you are to product-market fit.

Measuring product-market fit. Key metrics

So what are the metrics that you should look at when striving to reach product-market fit? There will be lots of formulas and details on each of them, but we’ll try to keep things as brief as possible.

1. PMFSurvey. How disappointed the users would be without your product?

Sean Ellis promotes this metric to be the main indicator of product-market fit. Product market fit survey is also known as the Sean Ellis test. The survey consists of asking one simple question to your customers, that is “How would you feel if you could no longer use X!?”. You can use this survey tool made specifically for this metric.

Image credit: pmfsurvey.com

If at least 40% of users would be very disappointed if they could no longer use your product, you got the “must-have” product and can start thinking about scaling it.

2. Churn rate. Do people want to stay with you or not?

The annual churn rate is the most common metric, and the standard benchmark is set at 5% . The number is not universal, though: each type of business has its own specifics, and new companies will typically have a higher churn rate than the established ones.

Note that the annual churn rate is very different from the monthly! Calculating monthly rates and comparing the dynamics is important, too, but make sure you don’t mix annual with monthly rates.

3. Net Promoter Score (NPS). How much do people like you?

This one is easier. NPS is calculated based on answers to the classic “On a scale of 0 to 10, how likely are you to recommend this company’s product or service to a friend or a colleague?”

Net promoter score formula

Companies with good product-market fit get at least 50 NPS . 50 is a great result for a SaaS product. This metric is basically a quantitative method of measuring word of mouth, which is so important for emerging products.

4. Growth rate. How many customers do you attract?

Growth rate can be calculated in revenue or number of users (for SaaS), and the time period can be different for each product.

Many people believe that if the business is growing, it goes in the right direction. However, things are not that simple. Having a high growth rate is good, but you have to be conscious of what makes it so high. If that is just the result of marketing investment, then it has little to do with product-market fit. 

How to analyze metrics?

Don’t worry if one of the product-market fit metrics is far from the benchmark. There are plenty of product-market fit examples that work despite the metrics. Here is just one of many cases: at the top growth rates of Facebook, their NPS was -14.

Metrics is the tool, not the goal. What really matters are the underlying causes that influence the overall situation and, consequently, the numbers.

There are so-called vanity PMF metrics , which means the numbers that look impressive but don’t truly influence the overall sustainability of the business. For example, having 10 million users is great, but what if no one pays for the premium? Doesn’t look that great anymore.

It happens when we look at separate metrics independently. For example, you have a really high growth rate. Awesome. The next thing you have to check is whether the churn rate is not higher than the growth, and then – whether the customer acquisition cost is balanced with customer lifetime value. You can be sure you’re doing great only when the metrics are balanced.

That’s why metrics should be plural. It may be tiring sometimes, but there is no single magical formula that shows your success (though just in case you found it, please drop us a line). And when all those metrics leave you confused, take a look at our list of best books on SaaS metrics with main take-outs from each of them.

Testing product-market fit

OK, so how do you do product-market fit testing? First of all, get your product ready. MVP is nice-to-have, but a prototype can work, too. Make sure you know what buyer personas you target.

If you want to test more than one hypothesis at once, you can set campaigns with different positioning points. But be careful with that option, as it requires additional experience.

So what do you need from the customers?

First of all, provide answers to the main questions of the product-market fit survey (see metrics #1 and #3 above).

Second, conduct in-person interviews . Many people find it intimidating and end up talking to the users they already know, leading to biased results. Survey participants need to be chosen randomly. You’d be surprised how many people are ready to dedicate some of their time to help you improve the product.

The way you phrase questions is also important. You won’t get any valuable information if you ask what people love about your product. Instead, focus on what is wrong, what could be improved.

The process of achieving product-market fit is basically testing the value hypothesis . If the hypothesis proved to be right, the metrics are fine, you have the PMF. If that is not the case, you have to iterate, change the product or change the focus market.

Changing the market might look like an easier option, but you should also be prepared to change both market and product, only if just slightly.

Finding or creating? Reaching the right audience

There are two approaches in finding the right market:

1. Focus on a particular niche/improving existing solution (example: Telegram as a better social media app)

2. Find an innovative solution (example: Netflix, as a new approach to video rental)

Many startup founders think that creating a new market is the best option. The most famous startups are the disruptive ones, giving solutions that the customers couldn’t have thought of. Airbnb, Apple, Uber, Netflix and similar companies were offering products to people unaware of such things, simply because it was nonexistent.

Whatever you choose, you have to find an existing problem and the solution to it. In one case, the solution is a completely new product, in the other, it is a more robust alternative.

So, is the whole process of finding product-market fit and working hard to achieve it worth the time and effort invested? Definitely yes! Let’s take a look at cases when product-market fit helped SaaS startups achieve business success.

Success stories: SaaS startups who achieved product-market fit

Adjusting or completely changing your product to meet market needs can sound intimidating.But once it’s done, it could lead to impressive results, as seen in examples below.

Superhuman is an email client solution. In 2017, they have been working on their MVP for two years already. The issue was that they never actually validated the problem and there was no clear product-market fit. And so when the product was almost ready to see the market, they tested it among the pre-launch audience and were quite upset with the result. Only 22% of users stated that they would be “very disappointed” if Superhuman was to shut down.

What did the Superhuman team do? They focused on the feedback provided by those 22% to improve the product. The first round of improvements turned out to be positive, increasing to 32% of users who would be disappointed if they couldn’t use Superhuman anymore.

The team then asked follow-up questions to determine how the email client should be improved. After they added new features and ran another survey, the results were amazing: now an impressive 58% of users wouldn’t want to see Superhuman go. Only after that the product was ready to launch and scale up. 

Segment is a B2B customer data platform. In 2011 they raised $600.000 from Y Combinator for their initial project called ClassMetric. This was an app that was supposed to collect students’ feedback. Each time students felt confused during a lecture, they pressed a button and the professors were able to see how many people got their material and how many needed additional explanation.

However, testing this idea showed that students don’t actually need an app for that. Users were fine with using Gmail or Facebook instead. The company didn’t validate the problem before developing an MVP and now had to quickly look for a new idea before they ran out of money.

Then the company tried to build Segment.io — their own version of Google Analytics, but with the focus on segmentation. Still, a year later they didn’t have any customers. 

Segment made a bold decision to ditch the existing project once again and instead focus on the open-source data library they built in the development process. But this time they didn’t go big. Instead, they created a landing page for the database with an email signup form and posted it to Hacker News to see if someone was interested. 

First positive responses came very quickly, validating the idea. Segment continued developing its database tool and succeeded, gaining a $3.2 billion exit .

Gamaya is an agricultural data processing company that was initially focused on farmers. They turned to Eleken to help them update their MVP, making its interface less clunky and confusing. We’ve managed to do that, creating a simple-looking yet informative platform.

But the story of our collaboration (and Gamaya achieving product-market fit) didn’t end with that. At some point, they realized that their product could also serve golf clubs that need to monitor the conditions of the fields.

This encouraged them to create a small product for golf clubs. Golf club owners could use Gamaya’s data platform to view their entire golf course and study its health. 

As amazing as this idea looked, its realization could have exceeded Gamaya’s budget. Luckily, Eleken designed their platform so it would be possible to expand it, using it as a basis for two different solutions. This, in turn, allowed Gamaya to target two different audiences at the cost of one product, making the most of their market research discovery. 

The role of UI/UX design in achieving product-market fit

So what role does UI/UX design play in a product-market fit? Basically, it helps you visualize your idea for future validation and increases the chances of its approval by both users and investors. 

Design largely contributes to that first impression people have when trying out your product. If a user experience is poor or an MVP looks outdated, chances are that both users and investors will decide that it’s not worthy of their time and attention. On the other hand, a modern look and great user experience will encourage them to look further into your product. 

Let’s take Astraea’s case as an example. It is an AI platform that makes it really easy for anyone to acquire, discover, and analyze satellite data at scale. They understood the importance of product-market fit and wanted to achieve it. To do this, they had to improve the quality of the platform’s user interface, making it attractive, usable, and consistent. They also wanted to add tailored applications to the existing products to increase potential value and impact on the customers.

Being a small team with no UI/UX designer in-house, they turned to Eleken for professional help. We redesigned the initial platform, helping it fit to the best standards of modern UX.

We also added the features that the client was looking for, such as Monitoring and EarthAI Notebook. After working with Eleken for over a year, Astraea was ready for big changes. The UI/UX improvements helped them launch a new business and gain interest from the investors.

Bottom line

Product-market fit means that your product truly meets the needs of the target market. To achieve it, companies test their products among the target audience to validate their hypothesis and improve their product. Achieving product-market fit takes time and potentially significant adjustments, but it is worth it as it helps your product stay relevant and generate stable profit.

Are you ready to start your journey to the product-market fit assessment?Then it's time to set up a plan for your team and build a product roadmap. We have an article full of tips and tools for building a product roadmap .

Masha Panchenko

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Value Hypothesis and Growth Hypothesis

Brett Hardin

Constantly Learning

Startups overuse hyped phrases. Lean, Scrum, Customer Development, and Minimum Viable Product are ubiquitous. The overuse of these phrases have perverted their meaning.

Only by going back to the phrases origin can you understand the meaning. The original books are a great place to start. Without reading the text people will have incorrect mental models of their meaning.

After reading the books, to correct my mental model, I believe most people who use the phrases have never read the source material.

Let’s ignore the terms we’ve all heard and focus on something different. Phrases not used — Value and Growth Hypothesis. If you are responsible for a product you need to understand these two concepts.

Note: I will be using the term product for the rest of this essay. However, you don’t have to sell a product. It could just as easily be a service.

The Lean Startup uses these phrases multiple times. Although I hear “minimum viable product” several times a day, I never hear the other two; which all came from the same book. Value and growth are two things successful startups focus on. When the startup discovers their product market fit (another over-used phrase) if they lose focus on identifying customer value, they fail.

Let’s position value and growth as hypotheses.

Value Hypothesis

Nothing of value is free.

Value comes before growth. The value a product provides is the only reason someone uses it. A value hypothesis tests if a product is valuable to potential customers. There are successful startups who no longer have a valuable product. These companies die.

A value hypothesis determines if a customer should adopt a product into their lives. A value hypothesis turns a value statement into a question.

• Statement: Customers use product X because they want to share their location with others.
• Question: Do customers use product X because they want to share their location with others?

Once you identify the question you need to test it. Then you can see if the results align with your assumptions.

Do not pull the wool over your own eyes.

If the results of your experiment don’t live up to your expectations, come up with a new value hypothesis to test. Your original value hypothesis was wrong. Customers identify and vocalize a product’s value, not employees of the organization.

A companies hypothesis has to align with the reason a customer uses the product. Awful things happen when a company has the wrong value hypothesis. The company creates features users don’t want. This misalignment is the primary reason companies fail.

There is good news. A product can have more than one value. For each different value identified and proved becomes another reason your product is useful.

Growth Hypothesis

Only focus on growth after you’ve tackled value. Don’t grow a product without value or you will burn out your brand.

A growth hypothesis is an assumption on how users find your product .

Do you have a growth hypothesis?

No? Then you are on the road to failure. You won’t bump into a growth strategy. Each product’s growth is unique. Anyone telling you different is full of shit.

If you have an idea for growth you should test it. Once tested, you won’t be guessing.

Instead of saying, “We will get users by doing X, Y, and Z,” you begin to say, “We are getting users through these methods which are already in place.”

If you already know the way your company is going to grow, test it. Don’t assume it works. Create a test, measure it, and prove it works. If it does work, keep the growth strategy active and come up with another way to grow even faster.

Dropbox was successful because they constantly tested growth hypotheses . When one of Dropbox’s assumptions (hypothesis) worked, they kept it and when one didn’t they abandoned it.

Are you doing something similar?

Always Changing and Improving

Hypothesis should be changing until a company proves them. When a company proves one hypothesis, they should come up with a new hypnosis and validate it too. The more value a product has the more users want to use it. The more proven growth strategies a product has the faster it grows.

A proven hypothesis can also improve. Determine a way to test it’s effectiveness. Always challenge your assumptions. Challenging your beliefs gives you better ways to answer questions.

Originally published at bretthard.in on July 10, 2012.

Written by Brett Hardin

I read, write, and create software. I optimize my life to learn. If I can help you, let me know. http://bretthard.in

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What is a Value Hypothesis?

Being able to show customers your value is one of the most powerful things you can do for your business. This is especially true when you’re going to market with a new product or solution. When prospects see the value of your new offering for their needs, your sales team will have an easier time closing the deal – with less negotiation and a lower rate of discounts.

The most important piece to this puzzle is ensuring that your target customer sees the value of your solution relative to its price. To achieve this, you need to start with a value hypothesis, which is simply an educated assumption about the value of your solution for your target customer and what your target customer is willing to pay for that value. You’ll articulate, test, and validate this hypothesis – and ultimately use this hypothesis to determine the product-market fit and optimize your customer value proposition.

What is a Value Hypothesis (and Why You Need One)?

A value hypothesis is an assumption about the value of your offerings and what your customers are willing to pay for that value. By definition, a hypothesis is an “educated guess” that you must then validate by testing said hypothesis with your target customer. A value hypothesis helps you systematically understand your customer’s needs so you can align your product or service with those needs. From there, you can communicate the value in a way customers and potential customers can truly understand.

How Do You Create and Validate a Value Hypothesis?

A value hypothesis should not be a wild guess. A solid value hypothesis should start with a baseline understanding of who your target customer is, what you think they care about, and the value you believe they would gain from your solution.

From there, you will validate, test, and refine the value hypothesis as you gain more understanding of your customer. As a result, the following process can be considered a cycle of continuous improvement as you learn more about your customers and refine and expand your offerings.  

1. Understand Your Customer’s Goals and Challenges

To get started, take stock of what you already know about your target customer. What are their demographics, interests, and behaviors? What are their biggest pain points–both as an organization and as an individual stakeholder? What are their goals? What milestones and metrics matter most to them?

One way to gather this type of information is by conducting a customer value analysis . A customer value analysis refers to the process of identifying the key drivers of customer value. This will help you create a strong value hypothesis based on your target customer’s needs and expectations.

2. Define Your Value

Once you understand your customer’s biggest pain points and motivators, you can begin to define your value around those driving factors. This involves mapping out the ways that you believe your solution relieves your customer’s biggest pain points and achieves the goals that matter most to them. Here again, remember that value isn’t created in a vacuum – it should always be informed by and co-created with your target customer. To learn more, see Co-Defining Customer Value .

3. Articulate Your Value Hypothesis

Now that you have aligned your solution to your customer’s goals and challenges, it’s time to articulate your value hypothesis. This should be a clear, concise statement that explains your assumptions and the value you expect your product or service will provide to the customer. Most importantly, your value hypothesis must be something you can test and prove.  

For example, if you are a SaaS platform that automates bookkeeping for small-to-medium businesses (SMBs), one example of a value hypothesis might be, “SMBs are willing to pay $100 per month to purchase our bookkeeping software to complete tasks faster and reduce errors with automated workflows.” This hypothesis is a concrete and quantifiable statement that you can test to determine whether your product is a good fit for your target market–and what they’re willing to pay for that value.

4. Test and Validate Your Value Hypothesis

The next step is to test and validate your value hypothesis. There are many ways to do this, but in the example above, you might start by surveying SMBs about their bookkeeping needs and ask them what they’d be willing to pay for a solution like yours. If the response aligns with your hypothesis, this is a good indication that your product is a good fit for the market, the value is clear, and it is priced right.

If there’s a significant mismatch in the response versus your expectation, you’ll need to take a closer look at why your solution isn’t resonating with your audience. In many cases, the problem is that your target customer does not believe the price and value are in alignment. In other cases, it might simply be the wrong audience for your solution.

5. Optimize Your GTM Strategy

Once you have tested and validated your value hypothesis, you can use what you learned to optimize your go-to-market strategy. Depending on the findings, this might involve changing your pricing, modifying your product offering, enhancing the customer experience, targeting a different audience, or simply refining your customer value proposition .

To get started with value-based selling, we invite you to get in touch with our team of experts. We are here, ready to help. Not ready to chat yet? See 5 Ways Value Selling is Reshaping the Sales Landscape .

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Value Hypothesis

What is value hypothesis.

A value hypothesis is a statement that outlines the value that a product or service will provide to its customers or users. It is a hypothesis because it is an assumption that needs to be validated through research and testing.

A value hypothesis is an important tool for product development because it helps to clarify the value that the product or service will provide to its target customers. By clearly articulating the value proposition, benefits, and differentiation, the product team can create a roadmap for development that focuses on creating a product that will meet the needs of its customers and provide value in the marketplace.

Build better products with user feedback self.__wrap_n=self.__wrap_n||(self.CSS&&CSS.supports("text-wrap","balance")?1:2);self.__wrap_b=(e,n,t)=>{t=t||document.querySelector(`[data-br="${e}"]`);let r=t.parentElement,o=f=>t.style.maxWidth=f+"px";t.style.maxWidth="";let c=r.clientWidth,a=r.clientHeight,s=c/2-.25,i=c+.5,p;if(c){for(o(s),s=Math.max(t.scrollWidth,s);s+1 {self.__wrap_b(0,+t.dataset.brr,t)})).observe(r):process.env.NODE_ENV==="development"&&console.warn("The browser you are using does not support the ResizeObserver API. Please consider add polyfill for this API to avoid potential layout shifts or upgrade your browser. Read more: https://github.com/shuding/react-wrap-balancer#browser-support-information"))};self.__wrap_n!=1&&self.__wrap_b("undefined",1)

Finding product-market fit, from the earliest stages through growth

At this year’s TechCrunch Disrupt, we assembled an all-star panel of venture capitalists working across the entire range of startup growth and got their insights on assessing product-market fit — a perennial and evergreen challenge for entrepreneurs at all levels of experience.

Human Ventures’ Heather Hartnett, Greylock’s David Thacker, and Felicis’ Victoria Treyger all shared their perspectives on what makes for good product-market fit, how to spot it and how to use it to your best advantage for both your business’ growth and for raising capital.

What to look for before there are even any metrics

Product-market fit gets easier to assess the further along in the company development process you are; it’s a lot simpler to figure out if what you’re offering is what your users want once you actually have users. But what about before that?

Especially for first-time founders, assessing product-market fit at a stage where it’s mostly anticipation can be as much art as science, but our panelists provided some advice about how to set yourself up for success.

Our view of product-market fit is at the earliest, earliest stages, really at that point, you’re looking for metrics that are not even there yet, right? You’re looking for the earliest indicators that customers even want what your messaging and value prop is, and then you have a strong hypothesis about what that value is, and you have a strong hypothesis about how you can grow that value over time. And then it’s just a lot of experiments to figure out if you can even see some of those early indicators. You know, my father used to say, “You don’t have to tell a thirsty man that he needs water.” So we always just think about “What is that thing that you’re not trying to double sell?” But it’s the single sell that people really do want.

Thacker added that while it may seem counterintuitive, it actually behooves entrepreneurs to raise as much money as possible on a concept in order to have the right resources from which to find and maximize product-market fit.

One piece of advice [ … ] is to raise money before you start building the product, right? Because that’s when investors are most excited about the idea and the promise, especially if you’ve had a great team and you want to raise enough money. I’ve seen some founders — as a VC, this is gonna sound self-serving me saying this — but I’ve seen some founders that raise way too little capital in their pre-seed round or their seed round. And they don’t give themselves enough time or enough runway to experiment, because I’ve rarely seen a product come out of the gate where, magically day one, there’s this amazing product-market fit [ … ] Even the most successful tech companies today, if you look at them, it was a pretty long journey for most of them to figure out what they need to do in their product to really get it to resonate with users, and which users it would resonate with. So my advice would be: Try to raise adequate funding to give yourself some runway for that, because one of the most challenging fundraising scenarios is where you raise some money, you haven’t really proven product-market fit, and you’re trying to get us to give more capital. And that can be a tough sell to investors.

Beyond raising money to power the journey of discovery of product-market fit, Treyger talked about the pre-indicators you might be able to find that would anticipate achieving it.

In the early days and the ideas phase, founders lean a little bit heavier into what’s happening in the world in macro and in your industry that really makes this problem. [Find] a very significant pain point and also lean into your own experience. Consistently, our best companies are ones where the founder identified the problem, while at company X. For example, our company Sentilink, the founders were battling fraud at a firm and they identified this new type of synthetic fraud that the bureaus couldn’t even identify because of a variety of different factors. And so before they went and built the product, they actually painted the picture of this very real and serious pain point that had developed that didn’t even exist 20 years ago. [ … ] Think from that unique vantage point that you sit: What are customers telling you? What’s missing today?

Taking the pulse of the customer

The discussion also veered toward ways to find out what your customers want, beginning with the “net promoter score” (NPS) metric favored by many looking to rank the organic growth potential of their product.

I think NPS is pretty consistent. Obviously, NPS matters, but so does how you measure it. NPS can be manipulated. So some companies will add a measure of how disappointed would you be if this product went away. So that’s a good measure. I personally like the data better. And the data, you can see in the data how frequently the customers are using the product, how they’re referring it, the level of expansion. So yeah, NPS is good. But I also like looking at the hard data that goes along with it.

Asked about whether to perform or commission market research with your target audience and use that to drive product design, Thacker suggested that it’s worth considering as one tool in your arsenal.

I think market research is a tool, as is user research. But I wouldn’t let it guide your company too much. I think it’s much better to get something out there in front of customers, see how they react [and] iterate very quickly on that. And as a startup, your speed is your ultimate advantage — move as quickly as possible. I think the best founders are guided by intuition. Sometimes that research confirms their intuition, and sometimes they ignore the research and they go with their intuition. And then I think the other thing that’s really important is to look at where the product’s working as you start to get data from users. Double down on the places where things are really working [ … ] One example from my past career: I used to work at LinkedIn, and for the first many years of LinkedIn, there was very little user engagement; we really hadn’t found product-market fit. We got millions of people to sign up for LinkedIn accounts, we had an amazing growth team. But once they signed up, they never came back to the site. What we noticed was, there was a very small percentage of users that were highly and actively engaged. And it turns out, on some exploration, these were recruiters, and they were using LinkedIn to find talent, because LinkedIn was an amazing resource for that. So we really doubled down on that use case, we built a tool set just for those users, and that ended up becoming LinkedIn’s largest business. So I think as you look across your data, the product may not work for everyone. But for the customers or users it is working for, figure out why and then really double down on those use cases.

Echoing Thacker’s comments around intuition, Hartnett shared that while metrics are definitely a signal, the key ingredient for early-stage investors is intuition and conviction.

We do have a metrics playbook that gives a guide to benchmarking where you could be, but it’s so much of an art and not a science. And if you as a founder have that conviction, because you have that intuition that’s being validated, that’s so much more appealing to early-stage investors, in my opinion. So don’t hesitate to lean into that and understand what’s really going to help you gain conviction yourself, because this is what you’re going to spend the next 10 years or 20 years of your life on. So don’t try to do it just to sell it to an investor, but really do it because that’s what’s happening.

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Institute for Digital Research and Education

How can I perform hypothesis tests in glm? | SPSS FAQ

Sometimes you may want to test hypotheses about the parameters after a linear regression analysis. On this page, we show a couple of examples of how to perform these hypothesis tests using the lmatrix and kmatrix subcommands in the glm procedure. These examples will use data set https://stats.idre.ucla.edu/wp-content/uploads/2016/02/hsb2-2.sav . Let’s say that we have run a linear regression model as follows.

glm write with female read math /print=parameter /design=female read math.

Written as a regression equation, we have the following:

write = b_0 + b_1* female + b_2* read + b_3* math , where b_0 = 11.896, b_1 = 5.443, b_2 = .325 and b_3 = .397.

 Example 1

Let’s say that we want to test if the coefficient for read is equal to the coefficient for math . The lmatrix subcommand allows us to specify our hypothesis test in terms of the linear combination of the regression coefficients. In our case, our null hypothesis is that b_2 = b_3, or equivalently, b_2-b_3 = 0. This leads to our lmatrix subcommand with 1 following the variable read and -1 following the variable math .

glm write with female read math /print=parameter /design=female read math /lmatrix = 'math = read' read 1 math -1.

In the output, we see the difference between the two parameters is -.072 = (.325  –  .397), as we expected. What the output also gives is the standard error for the difference and the confidence interval. The Test Results table shows the F-value and the p-value.

 Example 2

Let’s say that we want to test if the coefficient for female is equal to 4.2.  In order to do this, we need to use the kmatrix subcommand, because we are testing if the value is something other than 0.  You might want to do this, if, for example, you had regression coefficients from a previous model and you wanted to see if they were equal to the coefficients obtained with your current model.  To keep the example simple, we will test only one variable ( female ) in this example.

glm write with female read math /print=parameter /design=female read math /lmatrix = 'female' female 1 /kmatrix 4.2.

 Example 3

Let’s say that we want to test if the coefficient for female is equal to 4.2 and that the coefficient for read is equal to the coefficient for math . This will be a two degree-of-freedom test since there are two hypotheses that we want to test simultaneously.  Notice that the values specified on the kmatrix subcommand are listed in the same order as the tests listed on the lmatrix subcommand.

glm write with female read math /print=parameter /design=female read math /lmatrix = 'test' female 1; read 1 math -1 /kmatrix 4.2; 0.

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