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Why Cohort Analysis Is the Key to Better Product Marketing DecisionsWhy Cohort Analysis Is the Key to Better Product Marketing DecisionsWhy Cohort Analysis Is the Key to Better Product Marketing Decisions

In this Q&A, data scientist Arun Prem Sanker explains the benefits of segmenting customers into cohorts based on behavior patterns.

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Industry Perspectives

July 23, 2025

5 Min Read

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Alamy

By Lisa Bertagnoli

Data is big business. Each second spent browsing creates an average of 1.7 megabytes of data per person . Companies, however, can't do much with that data unless they can analyze and apply it to their business needs. That's where cohort analysis comes in. Cohort analysis is a method of organizing customer data into segments, or cohorts, based on factors such as age, geographic location, and frequency of use, and analyzes that data to reveal behavior patterns for each cohort. The result is richer, more detailed information companies can use to personalize and even hyper-personalize marketing efforts.

Arun Prem Sanker is a data scientist at Stripe and has more than 10 years of experience driving product growth and solving complex business challenges across diverse industries. His expertise includes experimentation, predictive modeling, machine learning (ML), and analytics. Prem Sanker has a proven track record of optimizing marketing strategies, improving conversion rates, and enhancing customer perception models.

In this Q&A, he offers best practices for using cohort analysis to better understand consumer behavior and enhance product marketing success.

Q: What are the main benefits of cohort analysis?

Prem Sanker: The biggest benefit is that it identifies behaviors within specific user groups. A Gen Z customer might behave differently than a Boomer. A power user who visits a site daily will display different behavior than an infrequent user. Behavior can also vary by geographic location. Cohort analysis enables companies to track the behaviors of specific groups, compare them with other groups, and tailor marketing efforts to each group accordingly. Understanding this behavior helps marketers improve retention rates and customer lifetime value.

Q: How does cohort analysis provide deeper insights than traditional analytics?

Prem Sanker: First, it enables companies to control for external factors, such as the seasons, the weather, or a pandemic. Let's say customer activity in a specific area of the country declines for a few days. An aggregate analysis might not reveal that the decline was due to a widespread internet outage, but cohort analysis by geographic areas would.

Second, cohort analysis helps distinguish between long- and short-term engagement patterns. For instance, a product like workflow software might experience a spike in sales at the beginning of a new year. Cohort analysis would reveal it as short-term behavior by a cohort of business owners starting the year by tapping their budgets for new technology.

Here are two real-life examples. Netflix uses cohorts to understand who watches a particular type of content, such as comedy. If a cohort watches comedy, the platform recommends other comedy-related content to that cohort. Another example is Airbnb, which targets information based on a cohort's travel preferences. A beach-loving cohort will get travel content centered on beach locations.

Q: What are the essential cohorts for companies to track?

Prem Sanker: First is the acquisition cohort, which groups customers according to the channel that brings them to the business. That could be a Google search, email, or a website. The acquisition cohort also includes information on when they became customers. The second is the behavior and engagement cohort, which groups customers by usage. For example, how frequently do they visit a site or buy a specific product? The third is the revenue cohort, which categorizes users based on the revenue they generate.

Q: What advanced segmentation techniques can enhance cohort tracking?

Prem Sanker: One technique is survival analysis , which predicts when users will "churn" or stop buying or using a product. Cohort-based retention rates make predictions based on customers who have already churned. Because it predicts the likelihood of churn over time, survival analysis enables companies to intervene and retain customers before they leave.

Behavioral clustering, another advanced technique, uses unsupervised ML techniques, such as hierarchical clustering, to segment users based on their behavior. For instance, a streaming platform could use behavioral clustering to identify users who stream content only on weekends or only watch on Friday afternoons. This approach gives companies more detailed information on users, and the more detail available, the better the data analysis.

A third approach involves recency, frequency, and monetary (RFM) analysis. RFM sorts customers by how recently they've visited a site, how frequently they visit it, and how much they spend during visits. Companies use this information to market to customers with similar RFM behaviors.

Q: How can companies set up a cohort analysis framework that meets their business needs?

Prem Sanker: First, identify the objectives and key results (OKRs). Does a business want cohorts based on usage, age, or geographic location? Then, choose the relevant metrics, including conversion rate, retention rate, and customer lifetime value. The conversion rate is the percentage of customers completing a transaction. The retention rate measures customer activity over a certain period. Customer lifetime value is the predicted revenue for a customer from the time they engage with the product to the time they churn. Product analytics data, marketing attribution data, and transactional data provide fodder for these metrics.

It's also important to hire and train employees with the skills needed to perform cohort analysis. Data scientists and product managers are essential, but marketing and engineering teams should also be integral to cohort analysis functions. Marketing teams can help guide OKRs because they know how to use the data to better target customers. Everyone needs the right tools, such as data visualization . Upskilling and training employees to use these tools correctly, along with competitive pay, will ensure the right talent is in place.

Finally, it's essential for businesses to be aware of and plan for a few data-related curveballs, including artificial intelligence (AI). AI is necessary for cohort analysis because data scientists use ML models to create cohorts. That said, the use of AI and AI agents to collect data raises privacy issues. Customers might love the idea of personalized marketing efforts yet harbor concerns about how much of their personal data is available for businesses to capture and use.

The Solution for Better Decisions

Concerns aside, cohort analysis far exceeds traditional data analysis in assisting companies in making the best decisions on how to reach a variety of customers. It's imperative for organizations to establish the right cohorts, train data scientists and product marketing employees to analyze the cohorts using the best tools possible, and use the data to personalize and hyper-personalize marketing messages. It's the most effective way to use data in an increasingly data-driven world.

About the author:

Lisa Bertagnoli is a writer and editor based in Wisconsin. She has written for a variety of trade and consumer publications, including Builtin.com, AARP Bulletin, and Crain's Chicago Business. Connect with Lisa on LinkedIn .