Your Quick Guide to Cohort Analysis for Reducing Churn11 min read

Our benchmark report found that the average SaaS company loses more than 50% of the customers it acquires within a month. But you know what’s worse than a high churn rate? Not knowing why customers are leaving. Without a solid cohort analysis strategy, you might be fighting symptoms instead of the actual problems causing churn.

This will lead to wasted resources, missed opportunities to meet customer needs, and stunted growth.

My goal with this article is to simplify cohort analysis and help you combat churn. I’ll cover the various types of cohort analysis and a 4-step process for conducting it code-free.

Let’s jump in!

What is the concept of cohort analysis?

Let’s first define what a “cohort” means. In the SaaS context, a “cohort” is a group of users who share a common characteristic within a defined period. For example:

• Users who signed up in January 2024.
• Customers who started a free trial in the same week.
• Trial users who upgraded to a premium plan in the same month.

Cohort analysis, then, is the process of examining the behavior of these groups over time.

Why is this analysis important? Aggregated revenue and usage metrics can be deceptive. For example, a sudden surge in new sign-ups might mask a decline in long-term retention. If you only look at total user numbers, you might see growth, but breaking it down by cohorts might reveal older users are churning at an alarming rate.

In addition to helping you pinpoint specific periods of user churn, cohort analysis is great for identifying patterns in user behavior, measuring the impact of product changes, and improving overall retention.

What are the two types of cohort analysis?

There are two main types of cohort analysis: behavioral cohort analysis and acquisition cohort analysis.

Both approaches allow you to track customer behavior and retention over time, but they differ in how they group users and what kind of questions they help answer.

Let’s examine them:

1. Behavioral cohort analysis

This method groups users based on shared actions or behaviors within your product. It helps you understand how certain behaviors impact user engagement and retention compared to others.

Let’s say you made a few changes to your onboarding flow and want to track the results. You could create two behavioral cohorts: users who completed the latest onboarding flow vs. users who were exposed to the older flow.

Track their engagement data over time to see if the change influenced your feature adoption rates.

2. Acquisition cohorts analysis

This type focuses on grouping users based on when and how you acquired them. Analyzing these cohorts helps you understand which acquisition channels are most effective at attracting high-value users.

For example, you could track weekly retention rates for users acquired through your top three customer acquisition channels. Then, note the channel that delivers the highest retention and optimize marketing spend accordingly.

While behavioral and acquisition cohorts are key for SaaS, it’s helpful to be aware of other cohort types that might be relevant depending on your specific needs:

• Time-based cohorts: Groups of users categorized by a specific time frame. These are useful for seeing how users who joined during specific promotions behave.
• Size-based cohorts: Groups of users based on the size of their account. Seeing how different account sizes interact with your tool can help you predict future revenue and prioritize development efforts.
• Demographic cohorts: Groups of users who share common demographic characteristics. This analysis provides a deeper understanding of how various user personas respond to marketing campaigns and engage with your tool when they eventually sign up.

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How to conduct a cohort analysis in 4 steps

Let’s walk through four simple steps we use to conduct cohort analysis at Userpilot.

1. Map the churn timeline

The first step is to group users by their sign-up date. I prefer to keep things simple by using a weekly or monthly timeline, but you can choose any period that fits your needs.

Next, track your user retention rates over time to identify any pattern.

Small drop-offs might be normal, but spikes require further analysis. For example, if users from the January cohort show a significant drop-off after three weeks, use tools like session replays and feedback analysis to investigate potential causes. This sudden drop could have resulted from onboarding issues, feature complexities, or other unique problems.

2. Find your sticky features

Identify key in-app events that indicate user engagement (e.g., account setup, invoice generation, and revenue reporting if you’re a subscription management tool).

Then, analyze how these events correlate with retention within the acquisition cohorts you created in step one. For instance:

• Hypothesis: Users who generate their first revenue report within the first week are more likely to remain active after three months.
• Analysis: Use an analytics platform like Userpilot to track the percentage of users in each cohort who performed this action and compare their retention rates.
• Actionable insight: If your hypothesis is correct, consider adding in-app prompts or educational content to encourage new users to generate their first revenue report sooner.

Besides testing hypotheses, you can also compare several events to understand which features drive the most long-term engagement. For example, compare the retention rate of users who use the “invoice” feature with users who interact with the “reporting” feature. If one is significantly less used, you can dig further to find the underlying reason and then decide whether to improve or sunset it.

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3. Dig deeper and compare behavioral cohorts

Churn is rarely caused by a single feature; it typically results from a combination of factors. So, compare your behavioral cohorts to see if there are any hidden trends and patterns affecting retention.

For example, you might find that users who completed your onboarding tutorial and integrated a third-party application within the first week have a significantly higher retention rate than those who only completed the tutorial. This suggests that third-party integration is a core need, and some users may be churning because they don’t know it exists.

4. Test, iterate, and optimize

Analyze the results from your cohort analysis reports and hypothesize changes that will improve the customer experience. Then, create a pilot test to see if users will respond positively before implementing those changes broadly.

Let’s return to the example in step three. One of the ways to improve your retention rates will be to incorporate third-party integration into your onboarding flow.

You can do this with a small cohort of users and A/B test against the control group who experience the original onboarding. Measure key metrics like integration completion rate, user engagement, and retention over a defined period.

If the A/B test shows a significant improvement in retention, roll out the change to all new users. However, if the results are inconclusive or negative, revisit your hypothesis and consider alternative solutions.

Cohort analysis in practice

Say you’re a product manager at a company called “CloudBoost,” which offers a project management tool. You’ve recently redesigned your onboarding flow, hoping to improve user activation and reduce churn. Now, you want to use cohort analysis to see if the changes have had a positive impact.

First, you’ll need to define your cohorts. You’ll only need two groups since you’re focusing on the onboarding redesign:

• Cohort A: Users who signed up before the onboarding redesign launch.
• Cohort B: Users who signed up after the onboarding redesign launch.

Use a tool like Userpilot to track activation, feature adoption, and retention rates for the next three months.

After gathering sufficient data, it’s time to compare the two groups.

• If Cohort B shows significantly higher activation and retention rates compared to Cohort A, it suggests the onboarding redesign was successful.
• If there’s no significant difference, or if Cohort B performs worse, you’ll need to investigate further using behavior analytics and in-app surveys. Perhaps users are having issues with a specific step in the new onboarding flow, or maybe the new design is confusing.

Userpilot can help you gather these insights and implement new changes code-free. Here’s what one of our customers has to say after using our platform for a while:

Run a customer cohort analysis with Userpilot

Userpilot’s intuitive nature makes cohort analysis easy and accessible, even if you’re not a data expert. Here’s how it can help you analyze different cohorts in three steps:

1. Capture user behavior effortlessly

Once you have the Chrome extension installed, you can begin tracking user actions like page visits and feature clicks without any extra setup or coding.

Userpilot also lets you create custom events to go beyond individual features and track the overall adoption progress. For example, you can measure how users complete complex flows involving multiple steps and use this insight to analyze more granular cohorts.

2. Build cohorts with precision

A common problem SaaS companies face with cohort analysis is gathering super-specific customer information. Userpilot solves this in many ways. With a few clicks, you can:

• Create cohorts based on user and company-level data.
• Define “start” and “return” events to measure retention (e.g., “Signed in” and “Invited teammate”).
• Filter events by user properties, company properties, and more.
• Choose the time interval (days, weeks, months) and date range for your analysis.

3. Visualize and analyze cohort data

Userpilot’s dashboards transform raw cohort data into clear, interactive visualizations that reveal actionable insights at a glance. Instead of struggling with complex spreadsheets, you can:

• Instantly identify trends and patterns with dynamic line graphs that show cohort behavior over time.
• Pinpoint areas of high engagement or churn with interactive heatmaps that highlight key data points.
• Drill down into specific user journeys by clicking on individual cells within the visualizations.
• Export your visualizations in various formats for easy sharing and reporting.

Conquer churn with cohort analysis

Cohort analysis helps you go beyond surface-level data and observe the often obscure patterns in user behavior. This equips you to make timely experience improvements and increase overall customer retention.

Ready to get started? Book a demo and see how Userpilot can empower you to gather customer data, perform cohort analysis, and build in-app experiences code-free.

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