7 Customer Analytics Case Studies (Updated for 2024)

Customer analytics is a subset of business intelligence that provides insights to help the business better serve its customers.

Ultimately, many customer analysts start in business intelligence or data analytics and build domain knowledge in customer strategy. Customer strategy knowledge is then assessed during interviews with a customer analytics case study. If you’re interested in a customer analytics role, here’s everything you need to know about landing a customer analyst job:

• What is Customer Analytics?
• What is a Customer Analytics Case Study?
• Interview Prep: How to Solve Customer Analytics Case Studies
• Sample Customer Analytics Case Study Interview Questions

Customer analytics is the measurement, collection, and analysis of data related to customers. The customer data may include demographics, psychographic segmentation, behavioral data, or customer feedback. Businesses then use the data to make marketing investments, operations, product development, and planning decisions.

Customer analytics relies on data from various sources, including website traffic data, Customer Record Management insights, and transactional and behavioral data. These data offer insights into a variety of business processes. For example, businesses can use customer analytics to:

• Identify opportunities for product/business development
• Minimize customer churn/predict churn
  
• Personalize product, service, or marketing offers
• Optimize pricing or product performance
• Improve customer experiences

Churn analysis, for instance, is a high-value-adding customer analytics problem. Businesses use it to identify customers most likely to churn from their platform or service. These insights can personalize the product, develop a new offer, or other strategies to improve retention.

Customer analytics relies on various data sources to answer important customer questions like Who are my most valuable customers? What strategies are most likely to increase conversion rates? What strategies are most likely to reduce churn?

Therefore, customer analytics can touch all business areas, from reducing acquisition costs to providing insights that increase retention and build loyalty.

Customer analysts must be proficient in data processing. Often, they are required to build complex datasets - both structured and unstructured data - to analyze customer behavior and product performance. The most common data sources used by customer analysts include:

• Social listening data
• Customer survey data
• Website traffic data
• Marketing analytics data
• Social media data
• Customer Resource Management (CRM) data

Customer analytics tools. Customer analysts must be proficient in various tools and have a strong sense of data processing techniques. Standard tools include analytics platforms like Google Analytics and CRMs like Salesforce, Tableau, or the Acquia Customer Data Platform.

Customer analysts must also have strong SQL skills to query data and pull customer insights, data visualization skills, analytical skills, and data sense.

Interviews for customer analyst roles typically include one or more customer analytics case studies. An interview case study is an open-ended discussion question that asks the interviewee to solve a real-life customer analytics case. These questions assess a wide variety of skills, including:

• Technical - SQL, data modeling (in some cases), A/B testing, and statistics
• Data Sense - Ability to pull valuable metrics, data processing and metrics, and ability to generate insights
  
• Behavioral - Communication and adaptability are assessed, as well as your problem-solving ability creatively.
  

For example, the interviewee might be asked: “How would you measure the customer service quality of a chatbox feature?” In addition, the interviewer would likely give a dataset for analysis.

As an interviewee, you would then need to propose a solution and perform an analysis to develop a solution for the problem.

To answer a customer analytics case study, you should use a framework to organize your response. A framework that includes asking for clarity, making assumptions about the case, gathering data and analysis, and ultimately proposing a solution will help you best communicate your ideas.

The most common steps we recommend for answering data science case studies include the following:

1. Clarify

Before you jump into an answer, you want to gather additional information. Data and insights about the customers are intentionally left out with analytics case study questions. Therefore, you must dig in and fill in the gaps in the provided data. Some questions to ask in customer analytics case interviews include:

• Who are the customers?
• What are the customer’s needs?
• How does the product/service align with these needs?
• Make Assumptions

At this stage, you can propose hypotheses about the case question. This stage shows your ability to develop customer insights. Remember always to communicate your hypotheses to the interviewer and walk the interviewer through your line of thinking.

2. Make Assumptions

At this stage, you can propose hypotheses about the case question. This stage shows your ability to develop customer insights. Remember always to communicate your hypotheses to the interviewer and walk the interviewer through your line of thinking.

At this stage, you might make assumptions about the following:

• Customer segments - What segments/demographics are most likely using the product?
• Customer desires - Why do customers buy the product or service? What are some reasons they might churn?
• Customer insights - How might you go about generating insights from the data? For example, what metrics would you investigate if you wanted to reduce customer churn?
  

3. Propose a Solution

In this step, you want to establish a hypothesis, which you will investigate. However, there isn’t one correct answer to this type of case question. Instead, these discussions are used to assess your ability to wrap your head around a problem quickly, your thoroughness in getting started, and, ultimately, how you generate insights from the data.

One tip: Your hypothesis is a refined version of the problem that uses the available data to support or disprove the hypothesis.

4. Provide Data Points and Analysis

The fundamental goal in this step is to choose and prioritize a key metric. This metric will allow you to work through the hypothesis and analyze different case solutions that will help you validate the idea.

In addition to performing analysis and gathering data, remember to discuss trade-offs. Your approach may have potential limitations, and incorporating these in your answer will show your thoroughness and ability to be proactive rather than reactive in assessing case studies.

As you prepare for a customer case interview, you can practice with these examples, including a range of customer analytics cases, analyzing churn behavior, identifying new customer outreach opportunities, and analyzing customer acquisition.

One thing to note: Customer analytics questions typically overlap with marketing analytics questions. Therefore, you might look at things.

1. How would you determine the best business partners for a credit card company?

More context: The company has a list of 100,000 small businesses but only has the human capital to reach 1,000 of them. How would you determine the best 1,000 businesses to reach out to?

This business case question is customer-centric; therefore, you could draw insights from existing business partners to determine the best new customers to target. You can follow along with this mock interview for this question:

2. What metrics would you use to determine the value of a marketing channel?

See the video solution here:

3. How would you verify that the map is showing the wrong location pickup spots?

Let’s say you work at Uber. You’re getting reports that riders are complaining about the Uber map showing wrong location pickup spots.

How would you go about verifying how frequently this is happening?

Note: If we only have user location data, we can’t know on what occasions the Uber map showed the wrong location pickup spots to riders.

However, we do know drivers and riders’ locations at different points in time. What happens with these variables when a driver arrives at the wrong pickup spot?

4. What three metrics would you suggest to monitor the health of an online community?

You work at Stack Overflow on the community team that monitors the health of the platform.

Community members can create a post to ask a question, and other users can reply with answers or comments to that question. The community can express their support for the post by upvoting or downvoting.

Now, think about what Stack Overflow’s objectives are. What would it mean for the community to be successful? What kinds of user actions does this translate to?

5.How would you investigate if the redesigned email campaign actually led to the increase in the conversion rate?

See video solution here:

6. How would you determine whether the carousel should replace store-brand items with national brand products of the same type?

To determine if the up-sell carousel should feature national-brand items instead of store-brand products, start by identifying a success metric, such as Margin per Add-to-Cart (ATC), to measure profitability.

You work as a data scientist for a grocery store chain that has a mobile app. At the end of the checkout process on the app, users are presented with an up-sell carousel, a sliding display of items that users can scroll/swipe to view and add to their cart. Currently, the carousel only presents items from the store’s personal brand.

7. How would you determine the customer service quality through the chat box for all interactions involving small businesses selling items to consumers?

View the video solution here: