Solving Retention Problems: A Strategic Framework

Retention is one of the most challenging aspects to tackle in any business. It’s complex, multifaceted, and requires a deep understanding of user behavior.

However, by taking a step back and leveraging data effectively, you can uncover patterns and differences between retained and non-retained users.

From my experience of having worked with multiple businesses on improving their retention, I’ve been able to come up with a three-step framework to help you systematically solve retention issues.

Let’s get into it.

Define Retention

The first step in solving retention problems is to clearly define what retention means for your specific context.

Are you focusing on Day 7 retention, Day 14, or monthly retention?

You also need to determine whether you are looking at bounded or unbounded retention.

Without a clear definition, your analysis will get messy and lead to inconclusive results.

Note: Please ensure that you have enough users in both the retained and not retained cohorts while defining retention. Without significant data in these groups, your analysis will not be meaningful.

Build cohorts and Analyze

Once you have a clear definition of retention, the next step is to create cohorts of retained vs non-retained users.

This is where the real analysis begins. Start by examining the basic characteristics of each cohort and gradually dive deeper into the data.

Key characteristics:

• Behavioral: How users interact with your product – engaging or performing actions within the product.
• User Persona: Demographics, channel of acquisition, job title, etc.

Study both these factors together to find meaningful insights. You’re rarely going to find good insights by studying them individually.

From a data standpoint, you have to understand 2 things:

• At what stage are the non-retained users dropping off from the product, and what was the reason? Did they face a bug? Did they not find what they were looking for?
• What is the distribution difference across factors (behavioral, user persona) for retained vs. non-retained users? For example, you might see that 80% of retained users were acquired via Google, but only 10% of non-retained users were acquired via Google. Or, 90% of retained users perform action X within 3 days of signing up, compared to only 5% of non-retained users doing so.

Note: Don’t jump straight into data. Create a hypothesis tree chart to list all potential factors that you feel could influence retention.

List out all possible hypotheses and explore each one thoroughly. Keep asking “why” and going deeper.

The deeper you go into your data, the better and more actionable insights you’ll find.

Turn Insights into Action

With insights in hand, the final step is to translate them into actionable ideas and experiments. This might involve tweaking product features, enhancing user onboarding, or personalizing user experiences based on the identified patterns.

Talk to the product team → develop experiments to test hypotheses → monitor impact on retention metrics → iterate based on results and continuously refine your approach.

Conclusion

Solving retention problems is an iterative process. It requires continuous effort and refinement. Don’t be disheartened if initial strategies don’t yield immediate results.

By systematically defining retention, analyzing cohorts, and translating insights into action, you can develop a robust strategy to enhance user retention.

Remember, retention is not just about keeping users but understanding and fulfilling their needs consistently.

Hope this was helpful. If you’re looking for any help with Mixpanel or analytics, feel free to reach out using any of the below methods.

LinkedIn | Email - anshdoesanalytics@gmail.com | Book a slot on my calendar