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4 Examples of Business Analytics in Action

• 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

About the Author

Top 20 Analytics Case Studies in 2024

Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to  Gartner CDO Survey,  the top 3 critical success factors of analytics projects are:

• Creation of a data-driven culture within the organization,
• Data integration and data skills training across the organization,
• And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

• AI in analytics: How AI is shaping analytics
• Edge Analytics in 2022: What it is, Why it matters & Use Cases
• Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

We will help you choose the best solution tailored to your needs:

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem's work has been cited by leading global publications including Business Insider , Forbes, Washington Post , global firms like Deloitte , HPE, NGOs like World Economic Forum and supranational organizations like European Commission . You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider . Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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5 Business Intelligence & Analytics Case Studies Across Industry

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

When businesses make investments in new technologies, they usually do so with the intention of  creating value for customers and stakeholders and making smart long-term investments. This is not always an easy thing to do when implementing cutting-edge technologies like artificial intelligence (AI) and machine learning. Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first  (a question at the core of one of Emerj’s most recent expert consensuses.)

Artificial intelligence and machine learning have certainly increased in capability over the past few years. Predictive analytics can help glean meaningful business insights using both sensor-based and structured data, as well as unstructured data, like unlabeled text and video, for mining customer sentiment. In the last few years, a shift toward “cognitive cloud” analytics has also increased data access, allowing for advances in real-time learning and reduced company costs. This recent shift has made an array of advanced analytics and AI-powered business intelligence services more accessible to mid-sized and small companies.

In this article, we provide five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.

1 – Global Tech LED :Google Analytics Instant Activation of Remarketing

Company description:  Headquartered in Bonita Springs, Florida, Global Tech LED is a LED lighting design and supplier to U.S. and international markets, specializing in LED retrofit kits and fixtures for commercial spaces.

How Google Analytics is being used: 

• Google Analytics’ Smart Lists were used to automatically identify Global Tech LED prospects who were “most likely to engage”, and to then remarket to those users with more targeted product pages.
• Google’s Conversion Optimizer was used to automatically adjust potential customer bids for increased conversions.

Value proposition:

• Remarketing campaigns triggered by Smart Lists drove 5 times more clicks than all other display campaigns.
• The click-through rate of Global Tech LED’s remarketing campaigns was more than two times the remarketing average of other campaigns.
• Traffic to the company’s website grew by more than 100%, and was able to re-engage users in markets in which it was trying to make a dent, including South Asia, Latin America, and Western Europe.
• Use of the Conversion Optimizer allowed Global Tech LED to better allocate marketing costs based on bid potential.

2 – Under Armour : IBM Watson Cognitive Computing

Company description:  Under Armour, Inc. is an American manufacturer of sports footwear and apparel, with global headquarters in Baltimore, Maryland.

How IBM Watson is being used:

• Under Armour’s UA Record™ app was built using the IBM Watson Cognitive Computing platform. The “Cognitive Coaching System” was designed to serve as a personal health assistant by providing users with real-time, data-based coaching based on sensor and manually input data for sleep, fitness, activity and nutrition.   The app also draws on other data sources, such as geospatial data, to determine how weather and environment may affect training.   Users are also able to view shared health insights based on other registered people in the UA Record database who share similar age, fitness, health, and other attributes.
• The UA Record app has a rating of 4.5 stars by users; based on sensor functionality, users are encouraged (via the company’s website and the mobile app) to purchase UA HealthBox devices (like the UA Band and Headphones) that synchronize with the app.
• According to Under Armour’s 2016 year-end results , revenue for Connected Fitness accessories grew 51 percent to $80 million.

3 – Plexure (VMob) : IoT and Azure Stream Analytics

Company description:  Formerly known as VMob, Plexure is a New Zealand-based media company that uses real-time data analytics to help companies tailor marketing messages to individual customers and optimize the transaction process.

How Azure Stream Analytics is being used:

• Plexure used Azure Stream to help McDonald’s increase customer engagement in the Netherlands, Sweden and Japan, regions that make up 60 percent of the food service retailer’s locations worldwide.
• Azure Stream Analytics was used to analyze the company’s stored big data (40 million+ endpoints) in the cloud, honing in on customer behavior patterns and responses to offers to ensure that targeted ads were reaching the right groups and individuals.
• Plexure combined Azure Analytics technology with McDonald’s mobile app, analyzing with contextual information and social engagement further customize the user experience. App users receive individualized content based on weather, location, time of day, as well as purchasing a and ad response habits. For example, a customer located near a McDonald’s location on a hot afternoon might receive a pushed ad for a free ice cream sundae.
• McDonald’s in the Netherlands yielded a 700% increase in customer redemptions of targeted offers.
• Customers using the app returned to stores twice as often and on average spent 47% more than non-app users.

4 – Coca-Cola Amatil : Trax Retail Execution

Company description:  Coca-Cola Amatil is the largest bottler and distributor of non-alcoholic, bottled beverages in the Asia Pacific, and one of the largest bottlers of Coca-Cola products in the region.

How Trax Image Recognition for Retail is being used:

• Prior to using Trax’s imaging technology, Coca-Cola Amatil was relying on limited and manual measurements of products in store, as well as delayed data sourced from phone conversations.
• Coca-Cola Amatil sales reps used Trax Retail Execution image-based technology to take pictures of stores shelves with their mobile devices; these images were sent to the Trax Cloud and analyzed, returning actionable reports within minutes to sales reps and providing more detailed online assessments to management.
• Real-time images of stock allowed sales reps to quickly identify performance gaps and apply corrective actions in store. Reports on shelf share and competitive insights also allowed reps to strategize on opportunities in store and over the phone with store managers.
• Coca-Cola Amatil gained 1.3% market share in the Asia Pacific region within five months.

5 – Peter Glenn : AgilOne Advanced Analytics

Company description:  Peter Glenn has provided outdoor apparel and gear to individual and wholesale customers for over 50 years, with brick-and-mortar locations along the east coast, Alaska, and South Beach.

How AgilOne Analytics is being used:

• AgilOne Analytics’ Dashboard provides a consolidated view across online and offline channels, which allowed Peter Glenn to view trends between buyer groups and make better segmentation decisions.
• Advanced segmentation abilities included data on customer household, their value segment, and proximity to any brick-and-mortar locations.
• Peter Glenn used this information to launch integrated promotional, triggered, and lifecycle campaigns across channels, with the goal of increasing sales  during non-peak months and increasing in-store traffic.
• Once AgilOne’s data quality engine had combed through Peter Glenn’s customer database, the company learned that more than 80% of its customer base had lapsed; they were able to use that information to re-target and re-engage stagnant customers.
• Peter Glenn saw a 30% increase in Average Order Value (AOV) as a result of its automated marketing campaigns.
• Access to data points, such as customer proximity to a store, allowed Peter Glenn to target customers for store events using advanced segmentation and more aligned channel marketing strategies.

Image credit: DSCallards

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Using people analytics to drive business performance: A case study

People analytics— the application of advanced analytics and large data sets to talent management—is going mainstream. Five years ago, it was the provenance of a few leading companies, such as Google (whose former senior vice president of people operations wrote a book about it ). Now a growing number of businesses are applying analytics to processes such as recruiting and retention, uncovering surprising sources of talent and counterintuitive insights about what drives employee performance.

Much of the work to date has focused on specialized talent (a natural by-product of the types of companies that pioneered people analytics) and on individual HR processes . That makes the recent experience of a global quick-service restaurant chain instructive. The company focused the power of people analytics on its frontline staff—with an eye toward improving overall business performance—and achieved dramatic improvements in customer satisfaction, service performance, and overall business results, including a 5 percent increase in group sales in its pilot market. Here is its story.

The challenge: Collecting data to map the talent value chain

The company had already exhausted most traditional strategic options and was looking for new opportunities to improve the customer experience. Operating a mix of franchised outlets, as well as corporate-owned restaurants, the company was suffering from annual employee turnover significantly above that of its peers. Business leaders believed closing this turnover gap could be a key to improving the customer experience and increasing revenues, and that their best chance at boosting retention lay in understanding their people better. The starting point was to define the goals for the effort and then translate the full range of frontline employee behavior and experience into data that the company could model against actual outcomes.

Would you like to learn more about our People Analytics ?

Define what matters. Agreeing in advance on the outcomes that matter is a critical step in any people-analytics project—one that’s often overlooked and can involve a significant investment of time. In this case, it required rigorous data exploration and discussion among senior leaders to align on three target metrics: revenue growth per store, average customer satisfaction, and average speed of service (the last two measured by shift to ensure that the people driving those results were tracked). This exercise highlighted a few performance metrics that worked together and others that “pulled” in opposite directions in certain contexts.

Fill data gaps. Internal sources provided some relevant data, and it was possible to derive other variables, such as commute distance. The company needed to supplement its existing data, however, notably in three areas (Exhibit 1):

• First was selection and onboarding (“ who gets hired and what their traits are”). There was little data on personality traits, which some leaders thought might be a significant factor in explaining differences in the performance of the various outlets and shifts. In association with a specialist in psychometric assessments, the company ran a series of online games allowing data scientists to build a picture of individual employees’ personalities and cognitive skills.
• Second was day-to-day management (“ how we manage our people and their environment”). Measuring management quality is never easy, and the company did not have a culture or engagement survey. To provide insight into management practices, the company deployed McKinsey’s Organizational Health Index (OHI), an instrument through which we’ve pinpointed 37 management practices that contribute most to organizational health and long-term performance. With the OHI, the company sought improved understanding of such practices and the impact that leadership actions were having on the front line.
• Third was behavior and interactions (“ what employees do in the restaurants”). Employee behavior and collaboration was monitored over time by sensors that tracked the intensity of physical interactions among colleagues. The sensors captured the extent to which employees physically moved around the restaurant, the tone of their conversations, and the amount of time spent talking versus listening to colleagues and customers.

The insights: Challenging conventional wisdom

Armed with these new and existing data sources—six in all, beyond the traditional HR profile, and comprising more than 10,000 data points spanning individuals, shifts, and restaurants across four US markets, and including the financial and operational performance of each outlet—the company set out to find which variables corresponded most closely to store success. It used the data to build a series of logistic-regression and unsupervised-learning models that could help determine the relationship between drivers and desired outcomes (customer satisfaction and speed of service by shift, and revenue growth by store).

Then it began testing more than 100 hypotheses, many of which had been strongly championed by senior managers based on their observations and instincts from years of experience. This part of the exercise proved to be especially powerful, confronting senior individuals with evidence that in some cases contradicted deeply held and often conflicting instincts about what drives success. Four insights emerged from the analysis that have begun informing how the company manages its people day to day.

Personality counts. In the retail business at least, certain personality traits have higher impact on desired outcomes. Through the analysis, the company identified four clusters or archetypes of frontline employees who were working each day: one group, “potential leaders,” exhibited many characteristics similar to store managers; another group, “socializers,” were friendly and had high emotional intelligence; and there were two different groups of “taskmasters,” who focused on job execution (Exhibit 2). Counterintuitively, though, the hypothesis that socializers—and hiring for friendliness—would maximize performance was not supported by the data. There was a closer correlation between performance and the ability of employees to focus on their work and minimize distractions, in essence getting things done.

Careers are key. The company found that variable compensation, a lever the organization used frequently to motivate store managers and employees, had been largely ineffective: the data suggested that higher and more frequent variable financial incentives (awards that were material to the company but not significant at the individual level) were not strongly correlated with stronger store or individual performance. Conversely, career development and cultural norms had a stronger impact on outcomes.

Management is a contact sport. One group of executives had been convinced that managerial tenure was a key variable, yet the data did not show that. There was no correlation to length of service or personality type. This insight encouraged the company to identify more precisely what its “good” store managers were doing, after which it was able to train their assistants and other local leaders to act and behave in the same way (through, for example, empowering and inspiring staff, recognizing achievement, and creating a stronger team environment).

Shifts differ. Performance was markedly weaker during shifts of eight to ten hours. Such shifts were inconsistent both with demand patterns and with the stamina of employees, whose energy fell significantly after six hours at work. Longer shifts, it seems, had become the norm in many restaurants to ease commutes and simplify scheduling (fewer days of work in the week, with more hours of work each day). Analysis of the data demonstrated to managers that while this policy simplified managerial responsibilities, it was actually hurting productivity.

The results (so far)

Four months into a pilot in the first market in which the findings are being implemented, the results are encouraging. Customer satisfaction scores have increased by more than 100 percent, speed of service (as measured by the time between order and transaction completion) has improved by 30 seconds, attrition of new joiners has decreased substantially, and sales are up by 5 percent.

The CEO’s guide to competing through HR

We’d caution, of course, against concluding that instinct has no role to play in the recruiting, development, management, and retention of employees—or in identifying the combination of people skills that drives great performance. Still, results like these, in an industry like retail—which in the United States alone employs more than 16 million people and, depending on the year and season, may hire three-quarters of a million seasonal employees—point to much broader potential for people analytics. It appears that executives who can complement experience-based wisdom with analytically driven insight stand a much better chance of linking their talent efforts to business value.

Carla Arellano  is a vice president of, and Alexander DiLeonardo is a senior expert at, People Analytics, a McKinsey Solution—both are based in McKinsey’s New York office;  Ignacio Felix is a partner in the Miami office.

The authors wish to thank Val Rastorguev, Dan Martin, and Ryan Smith for their contributions to this article.

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Business Analyst Case Study | Free Case Study Template

Business analyst case studies blog describes an actual business analyst case study. This provides real-world exposure to new business analysts.

In this blog, we will be discussing what is business analysis case study, why develop them, when to develop them and how to develop them. We will provide a real business case analysis case study for better understanding.

Let’s start with understanding what is business analysis before we go to analyst case studies.

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What is a business analysis case study 

Why prepare business analysis case study 

When to prepare business analysis case study

How to prepare business analysis case study

Example Business Analysis Case Studies

What is Business Analysis Case Study?

Before we try to understand, Business Analysis Case Study, let's understand the term case study and business analysis.

As per Wikipedia, a case study is:

"A case study is an in-depth, detailed examination of a particular case (or cases) within a real-world context."

For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like, world war, or more often the policy analysis of real-world problems affecting multiple stakeholders.

So, we can define Business Analysis Case Study as

"A Business Analysis case study is an in-depth, detailed examination of a particular business analysis initiative."

What is Business Analysis?

The BABOK guide defines Business Analysis as the “Practice of enabling change in an enterprise by defining needs and recommending solutions that deliver value to stakeholders”. Business Analysis helps in finding and implementing changes needed to address key business needs, which are essentially problems and opportunities in front of the organization.

Business analysis can be performed at multiple levels, such as at:

• The enterprise level, analyzing the complete business, and understanding which aspects of the business require changes.
• The organization level, analyzing a part of the business, and understanding which aspects of the organization require changes.
• The process level, analyzing a specific process, understanding which aspects of the process require changes.
• The product level, analyzing a specific product, and understanding which aspects of the product require changes.  

Why Develop Business Analyst Case Study

Business analysis case studies can be useful for multiple purposes. One of the purpose can be to document business analysis project experiences which can be used in future by other business analysts.

This also can be used to showcase an organizations capabilities in the area of business analysis. For example, as Adaptive is a business analysis consulting organization, it develops multiple business analysis case studies which show cases the work done by Adaptive business analysts for the client. You can read one such case study for a manufacturing client .

When To Develop Business Analyst Case Study

Business analysis case studies are typically prepared after a project or initiative is completed. It is good to give a little time gap before we develop the case study because the impact of a change may take a little while after the change is implemented.

Most professionals prepare business analysis case studies for projects which are successful. But it is also important to remember that not all changes are going to be successful. There are definitely failures in an organizations project history.

It is also important to document the failure case studies because the failures can teach us about what not to do in future so that risks of failures are minimized.

How To Develop A Business Analyst Case Study

Document business problem / opportunity.

In this section of the business analyst case studies, we discuss the actual problem of the business case analysis example.

ABC Technologies has grown rapidly from being a tiny organization with less than 5 projects to one running 200 projects at the same time. The number of customer escalations has gone up significantly. Profitability is getting eroded over a period of time. Significant management time is spent in fire-fighting than improving the business.

Top management estimated a loss of 10% profitability due to poor management of projects which is estimated at about 10 Million USD per annum.

Document Problem / Opportunity Analysis

For our above business problem, we captured the following analysis details.

Discussions with key stakeholders revealed the following challenges in front of ABCT management:

• There is very little visibility of project performances to top management
• Non-standard project reporting by various projects makes it harder for top management to assess the correct health of the project
• Practically there is no practice of identifying risks and mitigating them
• Project practices are largely non-standardized. Few project managers do run their projects quite well because of their personal abilities, but most struggle to do so.
• Due to rapid growth, management has no option but to assign project management responsibilities to staff with little or no project management experience.

Document Identified Solutions 

Based on root cause analysis, management decided to initiate a project to standardize management reporting. This required the organization to implement a project management system. The organization initially short-listed 10 project management tools. After comparing the business needs, tools, their costs, management decided to go with a specific tool.

Document Implementation Plan

The purchased tool lacked integration into the organizations existing systems. The vendor and organization’s IT team developed a project plan to integrate the new system with the existing systems.

Document Performance Improvements 

After a year, the effectiveness of the project was assessed. Projects showed remarkable improvement wrt reduced customer escalations, better on-time billing, and better risk management. The system also allowed the organization to bid for larger contracts as the prospective customers demanded such a system from their suppliers. The application was further enhanced to cater to the needs of other businesses in the enterprise as they were different legal entities, and their policies were different.

Document lessons learnt

Some of the key lessons learnt during this business analysis initiative were:

1. Stakeholder buy-in in extremely important to the success of the project

2. It is always better to go with iterative approach achieve smaller milestones and then go for larger milestones

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Taught by Ryan Buell (HBS), Dennis Campbell (HBS), and Jan Hammond (HBS)

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• Asynchronous Coursework and Assignments: 7 Hours
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Programming and Data Systems

Modern business analytics requires executives and managers to be conversant with programming, AI, and data architecture. The aim of this course is to provide participants with the fundamental knowledge and practice needed to appreciate the challenges and opportunities related to developing robust and scalable systems that are at the core of business analytics by emphasizing mastery of high-level concepts and design decisions. Through a mix of technical instruction, discussion of case studies, and weekly programming projects, this course empowers participants to make technological decisions even if not technologists themselves. Topics include Artificial Intelligence, cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies. Participants will learn the basics of coding with SQL and Python, and be introduced to fundamental concepts in decision trees, neural networks, LLMs, other types of AI models, and generative AI in order to understand what AI can do for their organization. PDS participants also have access to a custom AI-chatbot, which was built by the teaching team and designed to guide and aid participants, as a teaching assistant would.

Taught by Henry Leitner (SEAS) and David J. Malan (SEAS)

• Asynchronous Coursework and Assignments: 6 Hours
• Total: 8.5 Hours

Seminar II (2 Weeks)

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People analytics is designed to help practitioners use data to improve people-related decisions. Participants will build hands-on skills to analyze data in ways that complement the frameworks and intuitions they would normally use to guide their managerial actions on people issues. At a deeper level, participants in any job, organization, or industry context will sharpen their ability to think critically through the lens of rigorous analytics. Anchored in data, this course will equip participants with an analytic approach to diagnosing the varied forces that influence individual, team, and organizational performance, leading to more effective interventions and actions. While developing analytic skills and trying out tools and techniques, participants will come to appreciate the opportunities, limits, and tensions involved in using data analytics to inform people issues, while simultaneously gaining deeper insight into the substance of the business issues in question.

Taught by Jeff Polzer (HBS)

*You must complete Foundations of Quantitative Analysis, Programming and Data Systems, and Leadership, Innovation, and Change before you take this course.

• Asynchronous Coursework and Assignments: 9 Hours
• Total: 14 Hours

Term 3 (2 Courses, 8 Weeks)

Data-Driven Marketing

Marketing has been revolutionized and forever changed by data analytics. What used to be a qualitative and instinct-driven business function (think Mad Men ) has now become a data-driven profession that relies on quantitative insights on how best to optimize ad creation and placement and influence consumer purchase behavior. It also focuses on the benefits that AI and machine learning bring to marketing including enhanced personalization and customization, as well as pricing optimization and automation. You will also learn how AI affects change management and algorithmic bias. This course will examine the ways in which marketing has changed and the new skills and capabilities needed to succeed in this function.

Taught by Ayelet Israeli (HBS) and Flavio Calmon (SEAS)

*You must complete Competing in the Age of AI, Foundations of Quantitative Analysis, Operations and Supply Chain Management, Programming and Data Systems, Immersion 1, and Leadership, Innovation, and Change before you take this course.

Data Science Pipeline and Critical Thinking This course will take a holistic approach to helping participants understand the key factors involved in the data science pipeline, from data collection to analysis to prediction and insight. The curriculum will expand on the application of AI in data science by looking at the role of machine learning. Topics such as large language models, supervised learning, unsupervised learning, Bayes’ Theorem, and deep learning will be explored throughout the course. Projects will give participants hands-on experience developing and running a data science pipeline to ensure that the correct business predictions are being made.

Taught by Hanspeter Pfister (SEAS), Iavor Bojinov (HBS), and Mark Glickman (FAS) *You must complete Competing in the Age of AI, Operations and Supply Chain Management, Programming and Data Systems, and Foundations of Quantitative Analysis before you take this course.

Standard 24-Month Program Structure

Term 1 (1 course, 8 weeks), term 2 (1 course, 8 weeks), term 3 (1 course, 8 weeks).

• Asynchronous Coursework and Assignments: 4 Hours
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Term 4 (2 Courses, 8 Weeks)

Modern business analytics requires executives and managers to be conversant with programming and data architecture. The aim of this course is to provide participants with the fundamental knowledge and practice needed to appreciate the challenges and opportunities related to developing robust and scalable systems that are at the core of business analytics by emphasizing mastery of high-level concepts and design decisions. Through a mix of technical instruction, discussion of case studies, and weekly programming projects, this course empowers participants to make technological decisions even if not technologists themselves. Topics include artificial intelligence, cloud computing, networking, privacy, scalability, security, and more, with a particular emphasis on web and mobile technologies. Participants will learn the basics of coding with SQL and Python, and be introduced to fundamental concepts in decision trees, neural networks, LLMs, other types of AI models, and generative AI in order to understand what AI can do for their organization.

• Asynchronous Coursework and Assignments: 3.5 Hours
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Data Science Pipeline and Critical Thinking

This course will take a holistic approach to helping participants understand the key factors involved in the data science pipeline, from data collection to analysis to prediction and insight. The curriculum will expand on the application of AI in data science by looking at the role of machine learning. Topics such as large language models, supervised learning, unsupervised learning, Bayes’ Theorem, and deep learning will be explored throughout the course. Projects will give participants hands-on experience developing and running a data science pipeline to ensure that the correct business predictions are being made.

Taught by Hanspeter Pfister (SEAS), Iavor Bojinov (HBS), and Mark Glickman (FAS)

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Business Analysis Case Study: Unlocking Growth Potential for a Company 

Have you ever wondered what are the necessary steps for conducting a Business Analyst Case Study? This blog will take you through the steps for conducting it.

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Table of Contents  

1) An overview of the Business Analysis Case Study 

2) Step 1: Understanding the company and its objectives 

3) Step 2: Gathering relevant data 

4) Step 3: Conducting SWOT analysis 

5) Step 4: Identifying key issues and prioritising 

6) Step 5: Analysing the root causes 

7) Step 6: Proposing solutions and developing an action plan 

8) Step 7: Monitoring and evaluation 

9) Conclusion 

An overview of the Business Analysis Case Study  

To kickstart our analysis, we will gain a deep understanding of the company's background, industry, and specific objectives. By examining the hypothetical company's objectives and aligning our analysis with its goals, we can lay the groundwork for a focused and targeted approach. This Business Analysis Case Study will demonstrate how the analysis process is pivotal in driving growth and overcoming obstacles that hinder success. 

Moving forward, we will navigate through various steps involved in the case study, including gathering relevant data, conducting a SWOT analysis, identifying key issues, analysing root causes, proposing solutions, and developing an action plan. By following this step-by-step approach, we can address the core challenges and devise actionable strategies that align with the company's objectives. 

The primary focus of this Business Analysis Case Study is to highlight the significance of Business Analysis in identifying key issues, evaluating potential growth opportunities, and developing effective solutions. Through a comprehensive examination of the hypothetical company's strengths, weaknesses, opportunities, and threats, we will gain valuable insights that drive informed decision-making. 

By the end of this Business Analysis Case Study, we aim to provide a holistic view of the analysis process, its benefits, and the transformative impact it can have on unlocking growth potential. Through real-world examples and practical solutions, we will showcase the power of Business Analysis in driving success and propelling companies towards achieving their goals. So, let's dive into the fascinating journey of this Business Analysis Case Study and explore the path to unlocking growth potential for our hypothetical company. 

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Step 1: Understanding the company and its objectives  

In this initial step, we need to gain a thorough understanding of the hypothetical company's background, industry, and specific objectives. Our hypothetical company, TechSolutions Ltd., is a software development firm aiming to expand its customer base and increase revenue by 20% within the next year. 

TechSolutions Ltd. operates in the dynamic software solutions market, catering to various industries such as finance, healthcare, and manufacturing. The company's primary objective is to leverage its technical expertise and establish itself as a leading provider of innovative software solutions. This objective sets the foundation for our analysis, enabling us to align our efforts with the company's goals. 

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Step 2: Gathering relevant data  

To conduct a comprehensive analysis, we need to gather relevant data pertaining to the company's operations, market trends, competitors, customer preferences, and financial performance. This data serves as a valuable resource to gain insights into the company's current position and identify growth opportunities. 

For our case study, TechSolutions Ltd. collects data on various aspects, including customer satisfaction levels, market penetration rates, and financial metrics such as revenue, costs, and profitability. Additionally, industry reports, market research, and competitor analysis provide insights into market trends, emerging technologies, and the competitive landscape. This data-driven approach ensures that our analysis is well-informed and grounded in reality. 

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Step 3: Conducting SWOT analysis  

A SWOT analysis is a powerful tool to assess the company's internal strengths and weaknesses, as well as external opportunities and threats. By conducting a thorough SWOT analysis, we can gain valuable insights into the company's strategic position and identify factors that impact its growth potential. 

Step 4: Identifying key issues and prioritising  

In the case of TechSolutions Ltd., the analysis reveals two primary issues: an outdated technology infrastructure and limited marketing efforts. These issues are prioritised as they directly impact the company's ability to meet its growth objectives. By addressing these key issues, TechSolutions Ltd. can position itself for sustainable growth. 

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Step 5: Analysing the root causes  

To develop effective solutions, we must analyse the root causes behind the identified issues. This involves a detailed examination of internal processes, conducting interviews with key stakeholders, and exploring market dynamics. By identifying the underlying factors contributing to the issues, we can tailor our solutions to address them at their core. 

In the case of TechSolutions Ltd., the analysis reveals that the outdated technology infrastructure is primarily due to budget constraints and a lack of awareness about the latest software solutions. Limited marketing efforts arise from a shortage of skilled personnel and inadequate allocation of resources. 

Understanding these root causes provides valuable insights for developing targeted and impactful solutions. 

Master the art of Business Process Mapping and streamline your organisation's efficiency with our expert Business Process Mapping training !  

Step 6: Proposing solutions and developing an action plan  

For TechSolutions Ltd., the following solutions are proposed: 

a) Allocate a portion of the budget for technology upgrades and training: TechSolutions Ltd. should allocate a dedicated portion of its budget to upgrade its technology infrastructure and invest in training its employees on the latest software tools and technologies. This will ensure that the company remains competitive and can deliver cutting-edge solutions to its customers. 

b) Hire a dedicated marketing team and allocate resources for targeted campaigns: To overcome the limited marketing efforts, TechSolutions Ltd. should invest in building a skilled and dedicated marketing team. This team will focus on developing comprehensive marketing strategies, leveraging digital platforms, and conducting targeted campaigns to reach potential customers effectively. 

c) Strengthen partnerships with industry influencers: Collaborating with industry influencers can significantly enhance TechSolutions Ltd.'s brand visibility and credibility. By identifying key industry influencers and forming strategic partnerships, the company can tap into their existing networks and gain access to a wider customer base. 

d) Implement a customer feedback system: To enhance product quality and meet customer expectations, TechSolutions Ltd. should establish a robust customer feedback system. This system will enable the company to gather valuable insights, identify areas for improvement, and promptly address any customer concerns or suggestions. Regular feedback loops will foster customer loyalty and drive business growth. 

The proposed solutions are outlined in a detailed action plan, specifying the timeline, responsible individuals, and measurable milestones for each solution. Regular progress updates and performance evaluations ensure that the solutions are effectively implemented and deliver the desired outcomes. 

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Step 7: Monitoring and evaluation  

Conclusion  

In this detailed Business Analysis Case Study, we explored the challenges faced by a hypothetical company, TechSolutions Ltd., and proposed comprehensive solutions to unlock its growth potential. By following a systematic analysis process, which includes understanding the company's objectives, conducting a SWOT analysis, identifying key issues, analysing root causes, proposing solutions, and monitoring progress, businesses can effectively address their challenges and drive success. 

Business Analysis plays a vital role in identifying areas for improvement and implementing strategic initiatives. By leveraging data-driven insights and taking proactive measures, companies can navigate competitive landscapes, overcome obstacles, and achieve their growth objectives. With careful analysis and targeted solutions, TechSolutions Ltd. is poised to unlock its growth potential and establish itself as a leading software development firm in the industry. By implementing the proposed solutions and continuously monitoring their progress, the company will be well-positioned for long-term success and sustainable growth. 

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5 Real-World Business Analytics Examples That Prove the Value of Business Intelligence

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Studies conducted by McKinsey have been showing the same result for years: businesses that base their decisions on gut feeling are more likely to fail. On the other hand, companies that invest heavily in collecting and analyzing are more likely to outperform their competitors, acquire more customers, and become more profitable.

Instead of speaking about these findings in theory, we’re going to share several real-life examples to make case for business intelligence and show you how to leverage business analytics for growth.

The Importance of Business Analytics for Business Strategy and Decisions

How often do companies analyze their business performance, business analytics types, 5 business analytics examples that prove the value of bi.

Most businesses worldwide understand the importance of business analytics for strategic decisions. According to a MicroStrategy study, 57% of global enterprises have a CDO, Chief Data Officer, helping teams across the organization get more and better insights from the data available.

The same study found that over 70% of international companies had planned to expand their investment in data and analytics.

Our recent survey showed similar results: most companies rely on data when making business decisions and building strategy. Out of 29 respondents, 51.72% were B2C services or products, 27.59% were B2B services or products, and 20.69% were agencies or consultants (marketing, digital or media).

Only around 3% of respondents claim they don’t take data analytics into account during the decision-making process.

For those who aren’t completely convinced just yet, we’ll break down the main reasons why data and analytics should be a vital part of strategy creation.

• Improved efficiency. Data provides an objective insight into the efficiency of your processes so you can optimize them. It allows you to identify bottlenecks and areas for improvement in how you manage your resources and people and automate specific steps in your processes that don’t require human intervention.
• Real-time insights. Data provides you with important business insights in real-time. This way, crucial decisions don’t need to be delayed, but made instantly and with confidence. There’s no waste of time and any issues can be fixed early on.
• Accurate forecast. Comparing different data sets can help you identify trends and create more accurate forecasts for the future. Based on this forecast, you can act proactively to prevent or mitigate risks or seize opportunities, accurately predicting the results of your activities.
• Increased revenue. When you rely on data analytics to optimize your business operations, marketing campaigns, sales pipeline, and more, you can expect a higher revenue ( 8% on average ) and lower costs (10% on average).

Related : Business Intelligence Reporting: Definition, Benefits and Best Practices

Databox ran a survey to find the answer to the question “how often do companies analyze their business performance.”

Most of our respondents are B2C and B2B product and service providers, with marketing, digital, or media agencies making around 20% of participants. According to our survey results, most companies analyze their business performance at least monthly. A bit over 40% of survey participants said they conduct this analysis once a week.

You can conduct a business analysis by applying the four primary quantitative methods that allow you to interpret your data and gain insights from it: descriptive, diagnostic, prescriptive, and predictive analytics.

Our respondents top-ranked descriptive analytics (interpretation of historical data to identify trends and patterns), followed by diagnostic (identifying the root cause of a problem), prescriptive (determining which outcome will yield the best result in a given scenario) and predictive (forecasting future outcomes), respectively.

Let’s take a closer look at each type.

Descriptive Analytics

When using the descriptive analytics method, you simply summarize historical or present data with the goal to gain insight into the current business situation. This analysis allows you to identify patterns in your data and is useful when you need to report on past performance to managers or stakeholders.

The descriptive analytics method is a good starting point for new businesses that don’t know yet what strategies work and what don’t bring satisfactory results.

“We are in the early stage of company maturity, and most of our activities are focused on understanding which approaches work and which don’t. With descriptive analytics, we can get some quick insights into how close our business performance is next to the KPIs we set. We can also see which areas need more focus to achieve the best results in terms of growth,” explains Marcin Bartoszek of Spacelift .

Diagnostic Analytics

Once you have an overview of what happened in the past with your business performance, or what is going on at the moment, it’s time to dig into why it’s happened. That’s where diagnostic analytics steps in.

Here, you can drill-down into your data sets and look for correlations and causations to determine what factors contributed to specific results. When using diagnostic analytics, you can discover the root cause of an issue that’s been affecting your performance or why a campaign failed.

“Identifying problems and opportunities for optimisation is where we get the most value from our analytics,” says James Kinsley of Incendium AI . “Highlighting sticking points for particular segments of users, and improving our user journeys to maximize conversion has helped us most. There have been numerous times analytics has highlighted very expensive issues that happened with some new website updates, blocking conversions for certain users for example.”

Prescriptive Analytics

Prescriptive analytics include various techniques that businesses use to come up with recommendations for next steps that will improve their performance. Based on prescriptive analytics, a business can create a course of action in which various solutions can be tested to find the optimal one.

This optimal solution is most likely to lead the business toward achieving its goals, but needs to include regular feedback and repeated analyses to ensure the desired outcome.

“I chose Prescriptive first and foremost because it’s always best to know, when looking at analytics, which outcome would most likely help your business the best,” says Blake Brossman of PetCareRX . “This is followed by Descriptive because, to know which outcome would give you the best business, you have to look at the data that would figure that out.”

Predictive Analytics

Businesses use predictive analytics to forecast future events, figures, and outcomes. The forecast is based on the data you get during descriptive analysis and operates with probabilities rather than certainties.

Predictive analytics relies on machine learning and artificial intelligence and is commonly used in sentiment analysis: by analyzing opinions collected from social media, we can forecast how people will react to future products or services a business may launch. For many, predictive analytics are the most valuable type of business analytics.

Tom McSherry of Smuggs shares: “Predictive analytics help us identify future trends and patterns, so that we can make better decisions about where to allocate our resources. This information helps us improve our bottom line and grow our business.” explains McSherry and adds that the combination of different analytics methods usually generates the best (and most accurate) results:

“Descriptive analytics give us a clear picture of what has happened in the past, while prescriptive analytics help us determine the best course of action to take in the future. By combining all three types of business analytics, we are able to make more informed decisions that drive our business forward,” concludes McSherry.

PRO TIP: Are You Tracking the Right Metrics for Your SaaS Company?

As a SaaS business leader, there’s no shortage of metrics you could be monitoring, but the real question is, which metrics should you be paying most attention to? To monitor the health of your SaaS business, you want to identify any obstacles to growth and determine which elements of your growth strategy require improvements. To do that, you can track the following key metrics in a convenient dashboard with data from Profitwell:

• Recurring Revenue. See the portion of your company’s revenue that is expected to grow month-over-month.
• MRR overview. View the different contributions to and losses from MRR from different kinds of customer engagements.
• Customer overview . View the total number of clients your company has at any given point in time and the gains and losses from different customer transactions.
• Growth Overview . Summarize all of the different kinds of customer transactions and their impact on revenue growth.
• Churn overview. Measure the number and percentage of customers or subscribers you lost during a given time period.

If you want to track these in ProfitWell, you can do it easily by building a plug-and-play dashboard that takes your customer data from ProfitWell and automatically visualizes the right metrics to allow you to monitor your SaaS revenue performance at a glance.

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your Profitwell account with Databox. 

Step 3: Watch your dashboard populate in seconds.

We’re sharing five examples of how different companies used business analytics to make progress toward their goals in different aspects of business:

Deliver Outstanding Results to Clients

Optimize budget spending, save time for more meaningful tasks, achieve more goals, improve customer retention.

Business analytics can help you deliver outstanding results to your clients by creating in-depth reports with engaging data visualizations and actionable insights that the client can apply right away. This is particularly important for marketers (especially ones who run long-term games of content marketing and SEO), who often need more time to prove the value of their work.

Nextiny is a business growth and video agency that faced several challenges in client reporting: the agency was getting requests for tailored reports and had to track data in multiple tools, which caused many important insights to remain uncovered.

Using Databox features like Databoards , integrations with numerous data sources, and Data Calculations to track, view, and analyze the most relevant metrics, Nextiny managed to take client reporting to the next level. The agency is able to dive deep into customer data and provide more valuable and actionable insights to their clients.

As a result, Nextiny has 27 more clients than before doubling down on analytics .

Matthew Ramirez of Paraphrase Tool optimized his team’s spending by analyzing the data available in Google Analytics. “We were able to use data from our Google Analytics reports, which were incorporated into our business intelligence dashboards, to measure the effectiveness of our ads and determine that we were spending more on ads than we were making in revenue. We then pivoted to a subscription model (from advertising-based) and have seen a four-time increase in profit in just six months.”

Relying on business intelligence helps you make more informed decisions about your next steps, and make them quickly. Access to real-time data analysis allows you to act on issues early on before too much budget is wasted.

Deeplite is a Canadian AI software optimization startup. To be able to dwell deep into data and gain actionable insights from it, the team needed to access the numbers in real time, understand it, and create meaningful presentations for stakeholders in a digestible way. At the same time, as a startup, Deeplite needed to be able to act fast on the available data to optimize their limited budget. Especially if a campaign was underperforming.

Using dashboards and reports in Databox , Deeplite is able to monitor both high-level data and specific channels and build any dashboard in a few hours. Moreover, the whole team can access the dashboards any time and see real-time data without manual updating of spreadsheets, which helps team members to be aligned, make decisions fast, and prevent issues to make the most out of their budget.

Related : How to Set a Marketing Budget for a Small Business: 20 Tips

Business analytics saves your time because it eliminates guesswork and testing based on gut feeling. With data analysis, it becomes easier to determine what works and what doesn’t. If you give your team access to data, they can complete reporting-related tasks faster and have more time to focus on execution.

This is particularly important for fast-growing businesses that need to streamline their processes quickly to avoid mistakes. Harmon Brothers wanted to track the data from their internal and external campaigns, especially social ads, and use it to prove the value of their efforts to their clients. Using Databox allowed the team to dig deep into the data to understand exactly what’s happening. They were also able to set up goal-tracking, so they could compare tracked metrics to the goals they had set. By relying on the right data and analytics tools, Harmon Brothers cut reporting time per client per month by five hours.

As a result, the agency enjoyed an improved internal ROAS: from $1.5 to $2.6 .

Related : How to Automate Your Reporting Process with Databox

“Business analytics is a powerful tool that can be used to improve many different aspects of a business. When used correctly, it can help businesses reach their goals and create lasting success,” says George Harrison of Pkgmaker .

Collecting and analyzing data allows you to measure your progress more accurately and react promptly in case you notice your performance hasn’t been up to par. By fixing issues (and seizing opportunities) on the go, you’re more likely to achieve your business goals.

GMS, a business communication solution company working with over 900 mobile operators globally, sits on a lot of data generated every day, but without proper analysis, the team wasn’t able to identify trends and achieve goals. One of the problems was that only 60% of the company could understand, and therefore pull insights from the available data.

By working with Databox analytics and dashboard tools , GMS managed to make data accessible for 100% of their team members and improve achievement of their performance goals by 30%. Thanks to the intuitive interface, even non-data-scientists could interpret the data and create better data practices that ultimately led to better business outcomes.

PRO TIP : Learn how Kristina Simonson is leading her team in restructuring the way they approach KPI and goal setting at Privy.

Accurate and effective reporting is impossible without access to data. Businesses need to be able to look back on their past performance to identify errors and eliminate them in the future. Each mistake you eliminate and each gap you fill in your processes is one step toward higher customer engagement and retention.

This was the case of Elenas, an app designed for social media selling, was facing complex data with no way to dig into it efficiently. The 30-person team needed to track internal and external metrics and present their performance to the stakeholders and investors, ensuring a smooth customer experience from the first touchpoint to the last.

Using Databoards allowed Elenas’ team members to track all relevant metrics in terms of sales growth and customer satisfaction. Insight into data allows them to react quickly in case of an issue and reduce the number of escalations and customer complaints.

As a result, the team grew their customer retention rate from 22% to 57% and reduced the number of cancellations from 57% to 10%.

Related : 26 Effective Ways for Improving Your Customer Retention Rate

Leverage Your Data for Success with Databox

As you have seen, data lets you complete tasks faster, more accurately, and act in real-time to prevent issues and budget waste. But even if you have access to all the data you need, you still have to choose the right ally to help you collect and interpret the data in the most efficient way possible.

Over 20,000 businesses worldwide trust Databox to be their business analytics partner in growth. Easy to set up, use, and customize, our tool offers 100+ integrations that allow you to connect and automatically pull data from any data source. You can build and customize dashboards with no coding skills required, view automatically updated data in real-time, and share it with your team without spending hours on building complex reports.

Save 3+ hours on reporting every month, build beautiful dashboards with easy-to-understand metrics, and receive alerts and recommendations when your campaigns are underperforming to quickly get back on track.

Create a forever free Databox account today and monitor your business performance in one place.

Get practical strategies that drive consistent growth

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Home   »   Case Studies   »   Business Analytics for Financial Services

Business Analytics for Financial Services

In financial services, business requirements are complex and accuracy of information is paramount..

Deploying and optimizing a business analytics solution often involves significant systems integration challenges-so it’s important to engage a services provider with deep expertise. Here’s how IBM Premier Business Partner Mainline helped three very different financial services organizations turn their information into intelligence.

COMPANY:   Global insurance and financial services company HEADQUARTERS:   Northeast U.S. EMPLOYEES: 50,000

The Benefits:

• 3-fold more frequent reporting (monthly vs. quarterly)
• 75% reduction in time required to produce reports
• 90% fewer people involved in report production, enhancing productivity
• Improved accuracy of reports by reducing potential for human error
• Able to understand data better and faster, enhancing decision-making

The Business Challenge:

For years, this financial service company’s global compliance group struggled to manually collect data from multiple sources such as Excel spreadsheets, Word documents, and other report summaries. The lengthy compliance reports they needed to generate took weeks to compile, and with so many manual steps involved, accuracy was less than optimal; often, reports had to be re-run due to errors. The customer needed an end-to-end business analytics solution that would automatically collect and analyze a wide range of compliance metrics.

The Solution:

The company engaged Mainline to implement IBM Business Analytics and IBM DB2 for AIX data server to automatically collect data from source systems into a data mart and analyze compliance metrics. Mainline leveraged its expertise with the IBM Business Analytics Software Development Kit and provided an annotations manager tool to add more context into reports and tie comments back to other data elements-for example, adding dynamic notes to explain the reasons behind skewed or outlier data during a certain quarter. The solution eliminated the need to copy charts into Word and add associated verbiage.

The Result:

Mainline created a central data warehouse for all global compliance metrics with 14 sub-reports acting as one, reducing the time needed to collect data and product reports from weeks to days. Notes and charts can be produced at the same time as the report is executed. Users can choose which reports execute and change the chart structure on the fly to subjectively focus on relevant data points. They can understand data better and with much less effort. Role-based security in IBM Business Analytics allows business leaders to view only the data they are allowed to see. Mainline is now a trusted partner, and has been tasked with establishing an Analytics Center of Excellence.

Download the PDF

For more information, call your Mainline account representative or call Mainline directly at 866.490.MAIN(6246) or complete our contact us form.

COMPANY:   Midsize investment management firm HEADQUARTERS:   Northeast U.S. EMPLOYEES: 17,000

• 50% improvement in operational efficiency for generating client statements
• Created a highly customizable, user-friendly reporting environment
• Reduced demands on IT, enabling developers to focus on other projects
• Improved data accuracy
• Richer data helps customers understand how investments are performing

The reporting tool that an investment firm used for generating client statements was not meeting business requirements. The process of generating statements was complex, since data was based not only on the asset types that people owned, but also on variables that account executives had set up governing what they wanted their customers to see. In order to make the reports “pixel perfect” in terms of layout and positioning, developers from the IT staff had to be involved, taking time away from other internal projects.

Mainline provided a configuration utility to create a bridge between the legacy interface and IBM Business Analytics, solving the systems integration challenge. Separate reports have to come together and look like a unified document that has its own table of contents, and this required customization of IBM Business Analytics. The integration was an iterative process, developing and defining in tandem as the customer’s requirements changed. Mainline’s agility allowed IT to deliver exactly what marketing, client services, and other stakeholders wanted.

The customer now has enhanced functionality and flexibility in producing client statements. Investment portfolio statements can be generated much faster and contain more graphical depictions and footnoting than before, making them easier for customers to interpret. Account executives can add their own personalized annotations for customers, strengthening relationships. And because client statement generation is now entirely user driven, IT no longer needs to be involved. With richer data and reporting comes the potential for increased sales. The customer has increased its usage of IBM Business Analytics and is now developing a self-service reporting portal that will allow clients to generate reports online at any time.

COMPANY:   Diversified financial services company HEADQUARTERS:   Southeastern U.S. EMPLOYEES: 6,200+

• Seamlessly integrated multiple systems into a single user interface
• Provided the groundwork for better customer service
• Enhanced security
• Saved users valuable time with single sign-on
• Improved ability to recruit top-notch financial advisors

Having made a strong investment in Microsoft technologies, including SharePoint and SQL Server, a financial services company wanted to continue to use these tools for document management and workflow while implementing a powerful business analytics platform. The legacy portal that the customer was using was old and had no integration with SharePoint. Financial advisors had to locate reports in this separate system, which was often slow, and frequently they had to contact IT to resolve issues and get necessary reports. The customer needed more efficiency and interactivity in the reporting process.

Mainline conducted a highly customized implementation that integrated IBM Business Analytics with SharePoint and Microsoft SQL Server Analysis Services. The customer’s user interface requirements were to retain the Microsoft look and feel while creating an enterprise portal powered by IBM Business Analytics “behind the scenes.” Mainline’s expertise with the IBM Business Analytics Software Development Kit allowed it to achieve this level of integration between the IBM and Microsoft technology stacks, as well as a SiteMinder security appliance. Mainline drove the architecture and solutions while remaining agile, as business requirements were continually being revised.

Financial advisors now have direct access to all of their reports in a unified environment. Because it’s no longer necessary to go to different systems to pull reports, the advisors can present accurate reports to their clients instantly and in-person, improving client satisfaction. The reporting environment is more stable as well, and financial advisors now have a high level of trust in the data. Due to integration with the SiteMinder security appliance, user credentials are passed seamlessly down to the data source, eliminating the need for users to log in multiple times. The new portal is being used as a recruiting tool for financial advisors, and Mainline continues to provide support for change management requests as the customer’s business changes and grows.

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Top 10 Marketing Analytics Case Studies [2024]

The power of marketing analytics to transform business decisions is indisputable. Organizations leveraging these sophisticated tools gain unparalleled access to actionable intelligence that substantively impacts their financial outcomes. The scope of this invaluable resource extends from elevating the customer experience to fine-tuning the allocation of marketing budgets, presenting a spectrum of tactical possibilities. To explain the transformative impact and multifaceted benefits of employing marketing analytics, the article ventures into an in-depth analysis of five compelling case studies.

Each case is carefully selected to represent a distinct industry and set of challenges, offering a holistic understanding of how data-driven initiatives can surmount obstacles, amplify Return on Investment (ROI), and fortify customer retention metrics.

Case Study 1: How Amazon Boosted Sales by Personalizing Customer Experience

The situation: a tricky problem in early 2019.

Imagine it’s the start of 2019, and Amazon, a top name in online shopping, faces a confusing problem. Even though more people are visiting the website, sales are not increasing. It is a big deal, and everyone at Amazon wonders what’s happening.

The Problem: Complex Challenges

Figuring out the root problem was not easy. Amazon needed to know which customers weren’t buying stuff, their behaviors, and why the old methods of showing them personalized items weren’t working. It was a complicated issue that needed a smart and modern solution.

Related: Role of Data Analytics in B2B Marketing

The Solution: Using Advanced Tools

That’s when Amazon decided to use more advanced marketing tools. They used machine learning to understand different types of customers better. This insight wasn’t just basic info like age or location; they looked at how customers behave on the site, items left in carts, and trends based on where customers lived.

The Key Numbers: What They Tracked

To understand if the new plan was working, Amazon focused on a few key metrics:

1. Return on Investment (ROI): This showed the new marketing strategies effectiveness.

2. Customer Lifetime Value (CLV): This KPI helped Amazon understand how valuable customers were over the long term.

3. Customer Acquisition Cost (CAC): This measured how costly it was to get new customers.

4. Customer Retention Rate: This KPI showed how well they kept customers around.

5. Net Promoter Score (NPS): This gave them an idea of how happy customers were with Amazon.

The Results: Big Improvements

The new plan worked well, thanks to advanced marketing analytics tools. In just three months, Amazon increased its sales by 25%. Not only that, but the money they made from the new personalized ads went up by 18%. And they did a better job keeping customers around, improving that rate by 12%.

Lessons Learned: What We Can Take Away

So, what did we learn from Amazon’s success?

1. Personalizing Can Scale: Amazon showed that you can offer personalized experiences to a lot of people without sacrificing quality.

2. Track the Right Metrics: This case study clarifies that you must look at several key numbers to understand what’s happening.

3. Data Can Be Actionable: Having lots of data is good, but being able to use it to make smart decisions is what counts.

Related: Tips to Succeed with Marketing Analytics

Case Study 2: McDonald’s – Decoding Social Media Engagement Through Real-time Analytics

Setting the stage: a tantalizing opportunity beckons.

Imagine a brand as ubiquitous as McDonald’s, the global fast-food colossus. With its Golden Arches recognized in virtually every corner of the world, the brand had an expansive digital realm to conquer—social media. In the evolving digital arena, McDonald’s was trying to mark its presence and deeply engage with its audience.

The Maze of Complexity: A Web of Challenges

Steering the complicated world of social media isn’t for the faint-hearted, especially when catering to a customer base as diverse as McDonald’s. The challenge lay in disseminating content and in making that content strike a chord across a heterogeneous audience. The content must resonate universally, be it the Big Mac aficionado in New York or the McAloo Tikki enthusiast in Mumbai.

The Game Plan: A Data-driven Strategy

McDonald’s adopted a strategy that was nothing short of a data-driven symphony. Utilizing real-time analytics, the brand monitored a series of Key Performance Indicators (KPIs) to track the impact of its social media content:

1. Likes and Reactions: To measure immediate emotional responses from the audience.

2. Shares and Retweets: To gauge the virality potential of their content.

3. Impressions and Reach: To assess the scope and scale of engagement.

4. Click-Through Rates (CTR): To assess whether the content was sufficiently engaging to drive necessary action.

Types of content monitored varied from light-hearted memes to product promotions and even user-generated testimonials.

Related: Difference Between Marketing Analytics and Business Analytics

The Finale: Exceptional Outcomes and a Standing Ovation

The result? A whopping 30% increase in customer engagement on social media platforms within a quarter. But that’s not the end of the story. The customer retention rate—a metric critical for evaluating long-term brand loyalty—soared by 10%. These numbers didn’t just happen; they were sculpted through meticulous planning and real-time adjustments.

The Wisdom Gleaned: Eye-opening Insights and Key Takeaways

Several critical insights emerged from this exercise in digital finesse:

1. Agility is King: The fast-paced world of social media requires an equally agile analytics approach. Real-time monitoring allows for nimble adjustments that can significantly enhance audience engagement.

2. Diverse Audiences Require Tailored Approaches: The ‘one-size-fits-all’ approach is a fallacy in today’s digital age. Real-time analytics can help brands develop a subtle understanding of their diverse consumer base and tailor content accordingly.

3. Retention is as Crucial as Engagement: While the spotlight often falls on engagement metrics, customer retention rates provide invaluable insights into the long-term health of the brand-customer relationship.

4. Data Informs, But Insight Transforms: Data points are just the tip of the iceberg. The transformative power lies in interpreting these points to formulate strategies that resonate with the audience.

Related: VP of Marketing Interview Questions

Case Study 3: Zara—Harnessing Predictive Analytics for Seamless Inventory Management

The prelude: zara’s global dominance meets inventory complexities.

When you think of fast, chic, and affordable fashion, Zara is a name that often comes to mind. A retail giant with a global footprint, Zara is the go-to fashion hub for millions worldwide. However, despite its extensive reach and market leadership, Zara faced a dilemma that plagued even the most formidable retailers—inventory mismanagement. Both overstocking and understocking were tarnishing the brand’s revenue streams and diminishing customer satisfaction.

The Conundrum: A Dynamic Industry with Static Models

The fashion sector is a rapidly evolving giant, where the ups and downs of trends and consumer preferences create a landscape that is as dynamic as it is unpredictable. Conventional inventory systems, largely unchanging and based on past data, emerged as the weak link in Zara’s otherwise strong business approach.

The Tactical Shift: Machine Learning to the Rescue

Recognizing the inherent limitations of traditional approaches, Zara turned to predictive analytics as their technological savior. They implemented cutting-edge tools that used machine learning algorithms to offer more dynamic, real-time solutions. The tools were programmed to consider a multitude of variables:

1. Real-time Sales Data: To capture the instantaneous changes in consumer demands.

2. Seasonal Trends: To account for cyclical variations in sales.

3. Market Sentiments: To factor in the influence of external events like fashion weeks or holidays.

Related: MBA in Marketing Pros and Cons

The Metrics Under the Microscope

Zara’s analytics model put a spotlight on the following KPIs:

1. Inventory Turnover Rate: To gauge how quickly inventory was sold or replaced.

2. Gross Margin Return on Inventory Investment (GMROII): To assess the profitability of their inventory.

3. Stock-to-Sales Ratio: To balance the inventory levels with sales data.

4. Cost of Carrying Inventory: To evaluate the costs of holding and storing unsold merchandise.

The Aftermath: A Success Story Written in Numbers

The results were startlingly positive. Zara observed a 20% reduction in its inventory costs, a metric that directly impacts the bottom line. Even more impressively, the retailer witnessed a 5% uptick in overall revenue, thus vindicating their shift to a more data-driven inventory model.

The Gold Nuggets: Key Takeaways and Strategic Insights

1. Technology as a Strategic Asset: Zara’s case emphasizes that technology, particularly machine learning and predictive analytics, is not just a facilitator but a strategic asset in today’s competitive landscape.

2. The Power of Real-Time Analytics: The case reaffirms the necessity of adapting to real-time consumer behavior and market dynamics changes. This adaptability can be the distinguishing factor between market leadership and obsolescence.

3. Holistic KPI Tracking: Zara’s meticulous monitoring of various KPIs underlines the importance of a well-rounded analytics strategy. It’s not solely about cutting costs; it’s equally about boosting revenues and improving customer satisfaction.

4. The Future is Proactive, Not Reactive: Zara strategically moved from a reactive approach to a proactive, predictive model. It wasn’t merely a technological shift but a paradigm shift in how inventory management should be approached.

Related: Hobby Ideas for Marketing Leaders

Case Study 4: Microsoft—Decoding Public Sentiment for Robust Brand Management

Background: microsoft’s expansive reach and the perils of public opinion.

Microsoft is a titan in the technology industry, wielding a global impact that sets it apart from most other companies. From enterprise solutions to consumer products, Microsoft’s offerings span a multitude of categories, touching lives and businesses in unprecedented ways. But this extensive reach comes with its challenges—namely, the daunting task of managing public sentiment and maintaining brand reputation across a diverse and vocal customer base.

The Intricacies: Coping with a Data Deluge

The issue wasn’t just what people said about Microsoft but the sheer volume of those conversations. Social media platforms, customer reviews, and news articles collectively produced overwhelming data. Collecting this data was difficult, let alone deriving actionable insights from it.

The Playbook: Employing Sentiment Analysis for Real-time Insights

Microsoft addressed this issue head-on by embracing sentiment analysis tools. These tools, often leveraging Natural Language Processing (NLP) and machine learning, parsed through the voluminous data to categorize public sentiments into three buckets:

1. Positive: Which elements of the brand were receiving favorable reviews?

2. Negative : Where was there room for improvement or, more critically, immediate crisis management?

3. Neutral: What aspects were simply ‘meeting expectations’ and could be enhanced for better engagement?

Related: How to Become a Marketing Thought Leader?

Metrics that Mattered

Among the KPIs that Microsoft tracked were:

1. Net Promoter Score (NPS): To measure customer loyalty and overall sentiment.

2. Customer Satisfaction Index: To gauge the effectiveness of products and services.

3. Social Media Mentions: To keep a tab on the frequency and tonality of brand mentions across digital channels.

4. Public Relations Return on Investment (PR ROI) : To quantify the impact of their PR strategies on brand reputation.

Outcomes: A Leap in Brand Reputation and Diminished Negativity

The result was a 15% improvement in Microsoft’s Brand Reputation Score. Even more telling was the noticeable reduction in negative publicity, an achievement that cannot be quantified but has far-reaching implications.

Epilogue: Lessons Learned and Future Directions

Precision Over Ambiguity: Sentiment analysis provides precise metrics over ambiguous opinions, offering actionable insights for immediate brand management strategies.

1. Proactive Vs. Reactive: By identifying potential crises before they snowballed, Microsoft demonstrated the power of a proactive brand management strategy.

2. The ‘Neutral’ Opportunity: Microsoft found that even neutral sentiments present an opportunity for further engagement and customer satisfaction.

3. Quantifying the Intangible: Microsoft’s improved Brand Reputation Score underscores the value in quantifying what many consider intangible—brand reputation and public sentiment.

Related: Reasons Why Marketing Managers Get Fired

Case Study 5: Salesforce—Attribution Modeling Unlocks the Full Potential of Marketing Channels

Background: salesforce’s prowess meets marketing complexity.

Salesforce, synonymous with customer relationship management (CRM) and Software as a Service (SaaS), has revolutionized how businesses interact with customers. The company’s extensive portfolio of services has earned it a lofty reputation in numerous sectors globally. Yet, even this venerated SaaS titan grappled with challenges in pinpointing the efficacy of its myriad marketing channels regarding customer acquisition.

The Challenge: Decoding the Marketing Mix

Salesforce diversified its marketing investments across multiple channels—from search engine optimization (SEO) to pay-per-click (PPC) campaigns and email marketing. However, identifying which channels were instrumental in steering the customer through the sales funnel was a complex, if not convoluted, affair. The absence of a clear attribution model meant that Salesforce could invest resources into channels with subpar performance while potentially neglecting more lucrative opportunities.

The Solution: Attribution Modeling as the Rosetta Stone

To unravel this Gordian Knot, Salesforce employed attribution modeling—a sophisticated analytics technique designed to quantify the impact of each touchpoint on the customer journey. This model shed light on crucial metrics such as:

1. Last-Click Attribution: Which channel was responsible for sealing the deal?

2. First-Click Attribution: Which channel introduced the customer to Salesforce’s services?

3. Linear Attribution: How can the value be evenly distributed across all touchpoints?

4. Time-Decay Attribution: Which channels contribute more value as the customer gets closer to conversion?

The Dashboard of Key Performance Indicators (KPIs)

Among the KPIs that Salesforce monitored were:

1. Return on Investment (ROI): To calculate the profitability of their marketing efforts.

2. Customer Lifetime Value (CLV): To gauge the long-term value brought in by each acquired customer.

3. Cost per Acquisition (CPA): To understand how much is spent to acquire a single customer via each channel.

4. Channel Efficiency Ratio (CER): To evaluate the cost-effectiveness of each marketing channel.

Related: How to Become a Chief Marketing Officer?

Results: A Refined Marketing Strategy Paying Dividends

By adopting attribution modeling, Salesforce could make data-driven decisions to allocate their marketing budget judiciously. The outcome? A notable 10% surge in overall revenue and a 5% increase in ROI. The effectiveness of each channel was now measurable, and the insights gained allowed for more targeted and effective marketing campaigns.

Postscript: Reflective Takeaways and Industry Wisdom

1. Demystifying the Channel Puzzle: Salesforce’s approach elucidates that even the most well-funded marketing campaigns can resemble a shot in the dark without attribution modeling.

2. Customization is Key: One of the remarkable aspects of attribution modeling is its flexibility. Salesforce was able to tailor its attribution models to align with its unique business needs and customer journey.

3. Data-Driven Allocations: The campaign reveals the significance of using empirical data for budget allocation instead of gut feeling or historical precedents.

4. The ROI Imperative: Perhaps the most compelling takeaway is that focusing on ROI is not just a financial exercise but a strategic one. It affects everything from budget allocation to channel optimization and long-term planning.

Related: How Can CMO Use Marketing Analytics?

Case Study 6: Starbucks – Revolutionizing Customer Loyalty with Analytics-Driven Rewards

The backdrop: starbucks’ quest for enhanced customer loyalty.

Starbucks, the iconic global coffeehouse chain, is the most preferred place for coffee lovers. Renowned for its vast array of beverages and personalized service, Starbucks confronted a pivotal challenge: escalating customer loyalty and encouraging repeat visits in an intensely competitive market.

The Dilemma: Deciphering Consumer Desires in a Competitive Arena

In the dynamic landscape of the coffee industry, understanding and catering to evolving customer preferences is paramount. Starbucks faced the daunting task of deciphering these varied customer tastes and devising compelling incentives to foster customer loyalty amidst fierce competition.

The Strategic Overhaul: Leveraging Analytics in the Loyalty Program

Starbucks revamped its loyalty program by embracing a data-driven approach and deploying sophisticated analytics to harvest and interpret customer data. This initiative focused on crafting personalized rewards and offers, aligning perfectly with customer preferences and behaviors. The analytics framework delved into:

1. Purchase Patterns: Analyzing frequent purchase habits to tailor rewards.

2. Customer Preferences: Understanding individual likes and dislikes for more personalized offers.

3. Engagement Metrics: Monitoring customer interaction with the loyalty program to refine its appeal.

The Analytical Lens: Focused KPIs

Starbucks’ revamped loyalty program was scrutinized through these key performance indicators:

1. Loyalty Program Enrollment: Tracking the growth in membership numbers.

2. Repeat Visit Rate: Measuring the frequency of customer visits post-enrollment.

3. Customer Satisfaction Index: Gauging the levels of satisfaction and overall experience.

4. Redemption Rates of Offers: Understanding the effectiveness of personalized offers and rewards.

The Triumph: A Narrative of Success through Numbers

The implementation of analytics in the loyalty program bore significant fruit. Starbucks experienced a remarkable 20% increase in loyalty program membership and a 15% rise in the frequency of customer visits. More than just numbers, these statistics represented a deepening of customer relationships and an elevation in overall satisfaction.

The Crux of Wisdom: Essential Insights and Strategic Perspectives

1. Customer-Centric Technology: The Starbucks case highlights the crucial role of technology, especially analytics, in understanding and catering to customer needs, thereby not just facilitating but enriching the customer experience.

2. Personalization as a Loyalty Catalyst: The successful implementation of personalized rewards based on analytics underscores the effectiveness of customized engagement in enhancing loyalty.

3. Comprehensive KPI Tracking: Starbucks’ meticulous tracking of diverse KPIs illustrates the importance of a multi-dimensional analytics approach. It’s a blend of tracking memberships and understanding engagement and satisfaction.

4. Proactive Customer Engagement: Beyond traditional loyalty programs, Starbucks’ strategy shifts towards a proactive, analytics-based engagement model.

Related: Marketing Executive Interview Questions

Case Study 7: Uber – Revolutionizing Ride-Hailing with Predictive Analytics

Setting the scene: uber’s mission to refine ride-hailing.

Uber, a pioneer in the ride-hailing sector, consistently leads the way in technological advancements. To refine its operational efficiency and enhance the user experience, Uber faced the intricate challenge of synchronizing the supply of drivers with the fluctuating demand of riders across diverse geographical terrains.

The Challenge: Harmonizing Supply and Demand

The core challenge for Uber lies in efficiently balancing the availability of drivers with the dynamically changing needs of customers in different locations. This balancing act was essential for sustaining operational effectiveness and guaranteeing customer contentment.

The Strategic Move: Embracing Real-Time Data Analytics

In response, Uber turned to the power of real-time analytics. This strategic shift involved:

1. Demand Prediction: Leveraging data to forecast rider demand in different areas.

2. Dynamic Pricing Mechanism: Employing algorithmic solutions to modify pricing in real-time in response to the intensity of demand.

3. Driver Allocation Optimization: Using predictive analytics to guide drivers to areas with anticipated high demand.

Results: Measurable Gains in Efficiency and Satisfaction

The results of this approach, grounded in data analytics, were impressive. Uber saw a 25% decrease in average wait times for riders, a direct indicator of enhanced service efficiency. Additionally, driver earnings saw a 10% increase, reflecting better allocation of rides. Importantly, these improvements translated into higher overall customer satisfaction.

Related: Is Becoming a CMO Worth It?

Case Study 8: Spotify – Harnessing Music Analytics for Enhanced Personalization

Backstory: spotify’s pursuit of personalized music experience.

Spotify, the global giant in music streaming, sought to deepen user engagement by personalizing the listening experience. In a digital landscape where user preference is king, Spotify aimed to stand out by offering uniquely tailored music experiences to its vast user base.

The Challenge: Navigating a Sea of Diverse Musical Tastes

With an expansive library of music, Spotify faced the critical task of catering to the incredibly diverse tastes of its users. The task was to craft a unique, personalized listening experience for each user within a vast library containing millions of songs.

The Strategy: Leveraging Machine Learning for Custom Playlists

To address this, Spotify deployed machine learning algorithms in a multifaceted strategy:

1. Listening Habit Analysis: Analyzing user data to understand individual music preferences.

2. Playlist Curation: Employing algorithms to generate personalized playlists tailored to match the individual tastes of each user.

3. Recommendation Engine Enhancement: Continuously refining the recommendation system for more accurate and engaging suggestions.

Results: A Symphony of User Engagement and Loyalty

Implementing these machine-learning strategies led to a remarkable 30% increase in user engagement. This heightened engagement was a key factor in driving a significant rise in premium subscription conversions, underscoring the success of Spotify’s personalized approach.

Related: How Can Creating a Course Lead to Marketing Your Business?

Case Study 9: Airbnb – Advancing Market Positioning and Pricing with Strategic Analytics

Overview: airbnb’s quest for pricing and positioning excellence.

Airbnb, the revolutionary online lodging marketplace, embarked on an ambitious mission to optimize its global listings’ pricing and market positioning. This initiative aimed to maximize booking rates and ensure fair pricing for hosts and guests in a highly competitive market.

The Challenge: Mastering Competitive Pricing in a Diverse Market

Airbnb’s main challenge was pinpointing competitive pricing strategies that would work across its vast array of worldwide listings. The task was to understand and adapt to market demand trends and local variances in every region it operated.

The Strategic Approach: Dynamic Pricing Through Data Analytics

To achieve this, Airbnb turned to the power of analytics, developing a dynamic pricing model that was sensitive to various factors:

1. Location-Specific Analysis: Understanding the pricing dynamics unique to each location.

2. Seasonality Considerations: Adjusting prices based on seasonal demand fluctuations.

3. Event-Based Pricing: Factoring in local events and their impact on accommodation demand.

Results: A Story of Enhanced Performance and Satisfaction

This analytical approach reaped significant rewards. Airbnb saw a 15% increase in booking rates, indicating a successful price alignment with market demand. Additionally, this strategy led to increased revenues for hosts and bolstered customer satisfaction due to more equitable pricing.

Case Study 10: Domino’s – Transforming Pizza Delivery with Analytics-Driven Logistics

Background: domino’s drive for enhanced delivery and service.

Domino’s Pizza, a global leader in pizza delivery, set out to redefine its delivery efficiency and elevate its customer service experience. In the fiercely competitive fast-food industry, Domino’s aimed to stand out by ensuring faster and more reliable delivery.

The Challenge: Streamlining Deliveries in a Fast-Paced Environment

The critical challenge for Domino’s was ensuring timely deliveries while maintaining food quality during transit. It required a subtle understanding of logistics and customer service dynamics.

The Strategy: Optimizing Delivery with Data and Technology

Domino’s responded to this challenge by implementing sophisticated logistics analytics:

1. Route Optimization Analytics: Utilizing data to determine the fastest and most efficient delivery routes.

2. Quality Tracking Systems: Introducing technology solutions to track and ensure food quality throughout delivery.

Results: Measurable Gains in Efficiency and Customer Satisfaction

Adopting these strategies led to a notable 20% reduction in delivery times. This improvement was not just about speed; it significantly enhanced customer satisfaction, as reflected in improved customer feedback scores.

Conclusion: The Transformative Impact of Marketing Analytics in Action

Wrapping up our exploration of these five case studies, one unambiguous insight stands out: the effective application of marketing analytics is pivotal for achieving substantial business gains.

1. Personalization Works: The e-commerce platform’s focus on customer segmentation led to a 25% boost in conversion rates, underscoring that tailored strategies outperform generic ones.

2. Real-Time Matters: McDonald’s implementation of real-time analytics increased customer engagement by 30% and improved retention rates by 10%.

3. Forecast to Optimize: Zara’s application of predictive analytics streamlined inventory management, resulting in a 20% cost reduction and a 5% revenue increase.

4. Sentiment Drives Perception: Microsoft leveraged sentiment analysis to enhance its brand image, achieving a 15% rise in brand reputation score.

5. Attribution is Key: Salesforce’s adoption of attribution modeling led to a 10% revenue increase and a 5% boost in ROI, optimizing their marketing budget allocation.

These case studies demonstrate the unparalleled value of utilizing specialized marketing analytics tools to meet diverse business goals, from boosting conversion rates to optimizing ROI. They are robust examples for organizations seeking data-driven marketing decisions for impactful results.

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Table of Contents

Business analytics defined, applications of business analytics in various industries, business analytics applications, usage of business analytics, become a business analyst, business analytics applications and notable use cases.

Businesses today are faced with two very stark realities—the world is hyper-competitive, and data drive it. Companies that have the best information make the fewest mistakes, which in turn helps them to stay ahead of the pack.

Today’s digital society, through the explosion of Big Data and the Internet of Things (IoT) , has produced a ton of information. The challenge is to make any sense of all this data. With all of that information, who can sort out what’s useful and what’s not? That’s why business analytics is essential for today’s industries, and business analysts are in high demand. Today, we’re taking a look at popular business analytics applications, and some of the often-used cases.

Before launching into the meat of the matter, let’s take a moment to review. What’s the definition of business analytics? Business analytics involves the collating, sorting, processing, and studying of business-related data using statistical models and iterative methodologies. The ultimate goal is to glean practical and actionable business insights to solve an organization’s problems—boosting efficiency, productivity, and revenue.

Note that there’s a difference between business analytics and business intelligence (BI), though they are related. Business intelligence falls within the discipline of business analytics, the process of gathering the needed data from all sources, and preparing it for use by business analysts. In short, BI tells you what’s going on, and business analytics tells you why it’s happening and when it will occur again. So, a business analyst identifies a company’s weak areas, collects and sifts through data, creates a plan based on those findings, and helps to implement it.

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My experience with Simplilearn has been great till now. They have good materials to start with, and a wide range of courses. I have signed up for two courses with Simplilearn over the past 6 months, Data Scientist and Agile and Scrum. My experience with both is good. One unique feature I liked about Simplilearn is that they give pre-requisites that you should complete, before a live class, so that you go there fully prepared. Secondly, there support staff is superb. I believe there are two teams, to cater to the Indian and US time zones. Simplilearn gives you the most methodical and easy way to up-skill yourself. Also, when you compare the data analytics courses across the market that offer web-based tutorials, Simplilearn, scores over the rest in my opinion. Great job, Simplilearn!

I was keenly looking for a change in my domain from business consultancy to IT(Business Analytics). This Post Graduate Program in Business Analysis course helped me achieve the same. I am proficient in business analysis now and am looking for job profiles that suit my skill set.

Although business analytics is being leveraged in most commercial sectors and industries, the following applications are the most common.

Credit and debit cards are an everyday part of consumer spending, and they are an ideal way of gathering information about a purchaser’s spending habits, financial situation, behavior trends, demographics, and lifestyle preferences.

2. Customer Relationship Management (CRM)

Excellent customer relations is critical for any company that wants to retain customer loyalty to stay in business for the long haul. CRM systems analyze important performance indicators such as demographics, buying patterns, socio-economic information, and lifestyle.

The financial world is a volatile place, and business analytics helps to extract insights that help organizations maneuver their way through tricky terrain. Corporations turn to business analysts to optimize budgeting, banking, financial planning, forecasting, and portfolio management.

4. Human Resources

Although HR is often the punchline of many office jokes, its value in keeping a company successful is not to be underestimated. Great businesses are composed of a great staff, and it’s HR’s job to not only find the ideal candidates but keep them on board. Business analysts help the process by pouring through data that characterizes high performing candidates, such as educational background, attrition rate, the average length of employment, etc. By working with this information, business analysts help HR by forecasting the best fits between the company and candidates.

5. Manufacturing

Business analysts work with data to help stakeholders understand the things that affect operations and the bottom line. Identifying things like equipment downtime, inventory levels, and maintenance costs help companies streamline inventory management, risks, and supply-chain management to create maximum efficiency.

6. Marketing

Which advertising campaigns are the most effective? How much social media penetration should a business attempt? What sort of things do viewers like/dislike in commercials? Business analysts help answer these questions and so many more, by measuring marketing and advertising metrics, identifying consumer behavior and the target audience, and analyzing market trends.

As you can see, business analytics plays a valuable role in many different industries. You may also notice that some of the applications merge into each other, but that’s hardly surprising. By leveraging business analytics, multiple departments and teams can coordinate their efforts based on the information gathered and processed. It’s up to the business analyst to identify roadblocks and areas that need improvement, helping different departments to work together to achieve a common goal.

1. Customer Segmentation

Customer segmentation is a vital business analytics application that helps companies group their customers based on shared characteristics such as demographics, buying behavior, and preferences. By analyzing customer data, businesses can tailor their marketing strategies, product offerings, and customer service to target specific segments effectively, increasing customer satisfaction and overall profitability.

2. Predictive Analytics

Predictive analytics leverages historical and real-time data to forecast future trends and events. This application is used extensively in industries like finance, healthcare, and e-commerce for tasks such as predicting stock prices, patient outcomes, and product demand. It enables proactive decision-making, risk mitigation, and optimization of business operations.

3. Supply Chain Optimization

Businesses utilize analytics to optimize their supply chains by analyzing data related to inventory levels, supplier performance, transportation logistics, and demand forecasting. By identifying inefficiencies and bottlenecks in the supply chain, companies can reduce costs, improve product availability, and enhance overall operational efficiency.

4. Fraud Detection

Fraud detection analytics employs advanced algorithms and machine learning models to identify and prevent fraudulent activities, such as credit card fraud, insurance fraud, and cyberattacks. By analyzing transactional data patterns and anomalies, organizations can minimize financial losses and maintain the trust of their customers.

5. Market Basket Analysis

Market basket analysis involves examining customer purchase history to discover patterns in product co-purchases. Retailers use this application to optimize product placement, cross-selling, and promotional strategies. By understanding which products are frequently bought together, businesses can increase sales and enhance the customer shopping experience.

6. Churn Analysis

Churn analysis focuses on identifying and reducing customer churn, which is the rate at which customers stop using a company's products or services. By analyzing customer behavior and feedback, businesses can implement retention strategies to retain valuable customers and reduce revenue loss.

7. A/B Testing

A/B testing is a fundamental analytics application for optimizing digital marketing campaigns and website performance. It involves conducting controlled experiments by randomly assigning users to different versions of a webpage or marketing content. By comparing the performance of these versions, companies can make data-driven decisions to improve conversion rates and user engagement.

8. Employee Performance Analytics

Employee performance analytics helps organizations evaluate the productivity and engagement of their workforce. By analyzing data on key performance indicators (KPIs), attendance, and employee feedback, companies can make informed decisions about talent management, training, and workforce optimization.

9. Quality Control and Process Improvement

In manufacturing and production industries, analytics is employed to monitor product quality, detect defects, and optimize production processes. By analyzing data from sensors and production lines, businesses can reduce defects, improve efficiency, and minimize waste.

10. Sentiment Analysis

Sentiment analysis, also known as opinion mining, uses natural language processing and machine learning techniques to assess public sentiment and opinions from sources like social media, customer reviews, and surveys. Companies can gain insights into how their brand is perceived and use this information to shape marketing strategies and product development.

Become a business analysis expert with our Business Analyst Master's Program . Explore and enroll today!

These business analytics applications collectively empower organizations to make data-driven decisions , improve operations, enhance customer experiences, and stay competitive in today's data-centric business landscape.

Business analytics helps organizations run more efficiently and profitably. Here are six cases where business analytics proves its worth in the commercial sector.

1. Churn Prevention

Churn is the customer attrition rate, a percentage of subscribers, or customers who stop doing business with a company. Successful companies must keep the churn rate low and replace any customer losses that inevitably occur. Furthermore, it’s more expensive to acquire new customers than it is to retain existing ones. By using predictive analysis, a business analyst helps identify customer dissatisfaction and the most likely risks or departure.

2. E-Commerce Personalization

Online businesses, like Amazon, collect, process, and analyze customer data to personalize their customers’ shopping experiences. By customizing the experience, vendors can make recommendations and increase the likelihood of further sales.

3. Predictive Maintenance

Companies must face the inevitability of equipment maintenance, both scheduled and unplanned. Business analysts work with data to create metrics about maintenance lifecycles to predict future maintenance needs and avoid costly unplanned downtime.

4. Insurance Fraud Detection

Insurance fraud is costly to companies and their customers alike. This is especially true in the medical insurance industry, where fraud costs organizations in the US approximately $68 billion a year . Business analysts use big data to process billions of claims and billing records, enabling investigators to identify and mitigate any fraudulent activity.

5. Automated Candidate Placement

As mentioned earlier, hiring new staff comes with its share of risks and uncertainty. Business analysts leverage data-driven recruitment platforms to get a better picture of any given candidate—improving the likelihood of a successful job match much faster. In some cases, the information can even help anticipate job needs before a position is posted.

Simplilearn’s  Post Graduate Program in Business Analysis , in partnership with Purdue University, provides a unique Blended Learning method, which will equip you with all the latest skills and methodologies you will need to create a fantastic career in business analysis. You will also get to work on real-world projects with guidance from top experts from Purdue University and in the industry. Enroll today to become an AI-powered business analytics professional!

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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PowerMetrics Case Study: The Project Booth

Published 2024-04-11 , updated 2024-04-11

Summary - The ability for non-technical decision makers to independently access and analyze data is a benefit to all companies, regardless of their size. Larger organizations, with dedicated data teams, use PowerMetrics as a complementary tool within their existing data stack, enabling data experts to manage and curate the company’s data and metrics and business users to securely and easily access it for self-serve analytics. Smaller businesses, who may not have in-house data experts, can also benefit from the approachable analytics and metric-centric design found in PowerMetrics – All they need is a Partner.

Introduction

In this case study, Layne Booth, owner and CEO of The Project Booth, tells us how she and her team empower small businesses to make independent, data-driven decisions based on carefully selected, curated metrics and custom dashboards. By integrating PowerMetrics into their clients’ daily work, the Project Booth simplifies their analytics and transforms their data from an underused asset to a powerhouse of insight that drives growth and fuels efficiency and profitability.

About The Project Booth

At The Project Booth , a 100% woman-owned and operated company, we specialize in developing customized metric-centric dashboards for small businesses. Our goal is to provide our clients with the tools and insights they need to thrive in a competitive landscape. In real-world terms, we aim to boost our customers’ profitability within 90 days. Some of our client's success stories include growing manufacturing capacity by 25%, increasing profits by 33%, and tripling sales to $3 million. Starting with our 90 Day Dashboard Service for initial build and refinement, we offer additional support through video libraries for dashboard maintenance, monthly risk and opportunity reports, and fractional COO services for deeper operational guidance. 

Our services

Each client’s journey begins with a detailed data audit and analysis, focused on integrating with essential software platforms, such as QuickBooks, Xero, Asana, and popular social media channels, like Facebook, Instagram, and YouTube. This initial step ensures that we're not only connected to the most relevant data sources but also prepared to cleanse the data—identifying inaccuracies, duplications, and gaps to pave the way for efficient metric and dashboard implementation.

Our in-depth reporting highlights the factors that inform strategic decisions. For example, the use of ROI evidence to optimize marketing efforts and the benefit of adjusting inventory in reaction to sales trends. Clients also receive a 12-month Roadmap and Forecast plan to help them meet their annual sales objectives. Our expertise in building data feeds, consolidating spreadsheets, and designing bespoke dashboards are some of our key differentiators. We create visually appealing, easy-to-interpret dashboards tailored to each client's brand. Backed by the metric-centric architecture built into PowerMetrics, our dashboards aim to inform and engage our customers as they progress through their business journey. 

Working with metrics

The process of selecting a client’s key metrics, ones that align with their company’s values and objectives, is a collaborative one. This journey begins with a comprehensive "getting started" call, aimed at understanding the intricacies of their business, including their aspirations and challenges. This initial deep dive sets the stage for a thorough audit of their financial, marketing, and operational data, allowing us to unearth valuable insights and identify the metrics that truly matter in achieving their goals.

Selecting which KPIs to follow isn’t a unilateral decision; as with all client interactions, we work together to arrive at the ideal combination. Following the audit, we engage in a detailed discussion with our clients, sharing our findings, pointing out immediate opportunities for improvement, and suggesting key metrics to monitor. Through a process of iteration and fine-tuning, we arrive at a bespoke set of KPIs. 

While we firmly believe in customizing metrics to align with the unique goals and strategies of our clients, several key metrics frequently stand out, applicable across a variety of sectors:

• Revenue vs Forecast : This metric is crucial for evaluating the financial health and performance against expectations, providing a clear picture of where the business stands in relation to its financial goals.
• Revenue by Service/Product : Understanding which products or services are the biggest revenue generators helps in focusing efforts and resources effectively.
• Website Traffic and Referring Sites : These metrics offer insights into a company's online presence and the effectiveness of its digital marketing strategies, pinpointing where traffic originates.
• Email Newsletter Performance : Evaluating the success of email marketing campaigns, this metric can guide strategies for engagement and conversion through targeted communications.
• Social Media Followers and Engagement : This indicates not just growth in audience size, but also how interactive and responsive that audience is, reflecting the effectiveness of social media strategies.
• Ads Performance : By assessing the return on advertising spend, companies can refine their advertising strategies for maximum impact.
• Sale (Product or Service) Conversions : This metric highlights the effectiveness of the sales funnel, from lead acquisition to closing sales, providing insights into the customer journey and purchase process.

These metrics are foundational, yet adaptable, serving as a baseline from which we build a tailored approach to meet the specific needs and objectives of each client. This ensures they not only track their performance efficiently but do so with metrics that drive meaningful insights and outcomes.

Working with PowerMetrics

The Project Booth relies on PowerMetrics to help us craft professional, all-encompassing, automated dashboards for our diverse clientele. PowerMetrics, with its metric-centric approach and myriad features, enhances our team's and our clients' experience. Its cloud-based nature grants unparalleled access and flexibility, allowing both our team and our clients to interact with dashboards from anywhere, aligning perfectly with the dynamic needs of today's businesses.

The platform's robust user management capabilities ensure we can share valuable insights with our clients while safeguarding sensitive data, striking a fine balance between transparency and security. Thanks to Klipfolio's extensive API integration features, we're able to automate the data aggregation process from a wide array of sources, significantly reducing the need for manual updates and ensuring that dashboards reflect real-time information.

PowerMetrics’ user-friendly design and intuitive tools empower our team to tailor metrics and dashboards to meet the exact requirements of our clients. This ease of customization, coupled with the platform's facilitation of team collaboration, improves efficiency by streamlining our dashboard development process. 

Why PowerMetrics?

For us, PowerMetrics stands out for three main reasons: top integrations list (which continues to grow), ease of use and best-in-class customer service. 

Pivotal features in PowerMetrics center around data integration, metric customization, data accuracy, real-time updates, and universal accessibility.

From our team's perspective, the ability to build data feeds and to connect directly with a vast array of data sources is a good fit for SMBs that don’t use a data warehouse or even semantic layers. This connectivity allows us to efficiently aggregate data without the need for a data warehouse. Metric customization capabilities enable us to tailor dashboards to the specific needs of each client, ensuring the insights provided are both relevant and actionable.

Accuracy and the timeliness of updates are equally crucial, as they guarantee the reliability of the data, a necessity for informed decision making. 

From the client's perspective, the ease of accessing their dashboards anytime, anywhere is key. This feature helps them make agile decisions based on their current business metrics, regardless of their physical location. Clients also appreciate the way PowerMetrics brings clarity to complex data, significantly enhancing their ability to make fast, well-informed decisions. The importance of having accurate, up-to-date information cannot be overstated. It is the bedrock upon which our clients base their strategic and operational decisions. 

The Support Team at Klipfolio adds value to us and, by association, to our clients. They’re always available to help and their expertise and collaborative attitude make for a dynamic, flexible partnership.

How PowerMetrics became the core of what we do

PowerMetrics hasn't just changed our business; it's become the cornerstone of what we do at The Project Booth. Our mission is to help businesses grow by harnessing the power of dashboards, enabling them to use their data for competitive advantage. In today’s landscape of fleeting trends and "next big things," we've found our niche in something grounded and enduring: the strategic use of data for growth. The concept of dashboards may not seem new or revolutionary but the unique, metric-centric approach of PowerMetrics sets this BI solution apart.

Moving forward, we plan to help other agencies implement dashboard services to their respective clients. We strongly believe that if businesses, like marketing agencies, accounting firms, and operational services, add this incredible tool to their strategic client services, they’ll make more money for themselves and for their clients. 

Klipfolio’s analytics products

Klipfolio offers two cloud-based data analytics platforms:

• PowerMetrics is a complementary analytics solution that gives business users self-serve data access for independent, confident decision making. Centralized, certified metrics, governed by the data team, ensure data is accurate and aligned across all dashboards and reports.
• Klips is a traditional dashboarding BI solution that allows users to create and share real-time pixel-perfect data visualizations in dashboards and reports. You can use Klips to bring visualizations to your own internal teams and also to clients.

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