What is a cohort analysis, and how do I do one?

Ever wondered why some users stick around while others drop off after a day? Let's dive into the world of cohort analysis to uncover the secrets behind user behavior. It's not as daunting as it sounds—promise!

By segmenting users into groups based on shared characteristics, we can track how different cohorts interact with our product over time. This approach reveals patterns in retention, engagement, and churn that might be hidden in aggregated data.

Understanding cohort analysis

Cohort analysis is all about grouping your users based on common traits and seeing how their behavior changes over time. By tracking these cohorts, you can spot trends in things like user retention, engagement, and churn. It's a deeper dive compared to looking at general, lumped-together metrics.

This technique is super important for understanding why users stick around or bail. It helps pinpoint factors like acquisition channels, product features, or pricing plans that influence user behavior. Comparing different cohorts lets you see which strategies are boosting long-term retention and which ones might be causing early churn.

Unlike broad metrics that mush all users together, cohort analysis lets you see how different segments behave. This segmentation can reveal trends that get lost in overall averages. For instance, a high churn rate among new users might be hidden if you have a solid base of loyal users propping up your numbers.

Plus, cohort analysis lets you track how product changes impact users over time. By comparing cohorts before and after a new feature release or a UI tweak, you can gauge how those changes affect behavior. This insight is gold for making data-driven decisions to optimize your product.

At Statsig, we make this whole process easier. Our platform offers robust cohort analysis features that help you understand your users better and make smarter product decisions.

So now that we've got a handle on what cohort analysis is, let's explore the different types of cohorts you can use.

The different types of cohorts

When it comes to grouping users, there are two main types of cohorts: acquisition cohorts and behavioral cohorts.

Acquisition cohorts group users based on when they first interacted with your product—like their sign-up or purchase date. These cohorts help you track how user behavior and retention evolve over time, revealing trends in engagement and churn. Learn more about acquisition cohorts here.

On the flip side, behavioral cohorts segment users based on specific actions they've taken or their level of engagement within your product. Examples include:

• Power users who frequently use key features

• Churned users who have stopped using the product

• Resurrected users who came back after a break

Behavioral cohorts give you insights into how certain actions impact retention and revenue. By spotting behaviors that drive engagement, you can focus on features and strategies that encourage those actions. Check out more on behavioral cohorts.

Choosing between acquisition and behavioral cohorts depends on what questions you're trying to answer. Want to see how user behavior changes over time? Go with acquisition cohorts. Trying to figure out which actions boost engagement and retention? Behavioral cohorts are your friend.

How to conduct a cohort analysis

Ready to dive into cohort analysis? Here's a simple roadmap to get you started.

First up, analyze churn timing. Create acquisition cohorts and track when users drop off over time. This helps you spot critical points where churn happens.

Next, identify sticky features that keep users coming back. Look for correlations between feature usage and retention rates. By creating behavioral cohorts, you can see how different features and actions affect churn. Comparing these cohorts can reveal valuable insights.

With these findings, develop hypotheses about why users are churning and test changes iteratively. Maybe a certain feature isn't as engaging as you thought, or perhaps there's a hiccup in the onboarding process. Use data to guide your decisions, and keep refining your strategies.

Tools like Statsig's cohort analysis features can make this whole process smoother. Our platform offers flexible cohort definitions and real-time updates, so you can stay on top of user behavior.

Remember, cohort analysis isn't a one-and-done deal. It's an ongoing process of analyzing, testing, and optimizing. Combining cohort analysis with other techniques—like correlation and linear regression analysis—can give you a full picture of user behavior.

Applying cohort analysis to reduce churn and improve product decisions

So, how do you put all this cohort analysis goodness to work?

First, use your insights to enhance customer segmentation and targeted marketing. By understanding what makes your high-value cohorts tick, you can tailor your marketing efforts to attract and retain similar users.

Next, make changes to product features based on what you've learned. If you find that users who engage with a particular feature are more likely to stick around, focus on improving that feature. Encourage more users to check it out.

It's also crucial to continuously monitor cohort data. User behavior can shift over time, so keep an eye on the trends and adjust your strategies as needed. Again, Statsig's tools can help here, enabling you to track cohort performance over time and collaborate with your team.

By leveraging cohort analysis to fine-tune customer segmentation, optimize product features, and inform ongoing decisions, you can effectively reduce churn and drive growth. The key is to consistently apply these insights to better serve your users.

Closing thoughts

Cohort analysis is a powerful tool for uncovering the stories hidden in your user data. By grouping users and tracking their behavior over time, you can make smarter, data-driven decisions to improve your product and keep users engaged.

If you're looking to dive deeper, check out Statsig's resources on cohort analysis or start a blog to practice your skills and contribute to the community. Happy analyzing!

Hope you found this useful!