As a Product Manager at Blend, I've had my fair share of challenges in reducing mobile app churn. But it wasn't until I delved into user analytics that I discovered the true power of understanding user behavior. In this article, I'll share my lessons on how user analytics can help drive significant business value by reducing mobile app churn and improving app user experience.

Product managers know that understanding user behavior is crucial to driving value for users (and your business). However, relying solely on common sense and intuition can lead you down the wrong path. By combining quantitative user analytics with qualitative feedback, product managers can uncover the root causes of disengagement and churn, and drive product changes that unlock significant business value.

In my experience, users become disengaged for many surprising reasons. A case study I'll share later illustrates how identifying trends in user behavior and understanding "the why" helped us supercharge user engagement and reduce churn.

The Challenge: Reducing Churn for a Mobile App

As a Product Manager on a CRM mobile app for real estate agents, our goal was to become an indispensable tool for all real estate agents. We wanted agents to stay active in the app and do a better job selling real estate; our business model depended on it. However, our churn rate was high, with many agents registering, logging in a few times, and then leaving forever.

Unlocking Insights through User Analytics

To tackle this problem, we turned to user analytics. We analyzed overall user engagement, but that wasn't helpful either. Our previous approach involved experiments targeting every user in the app, which didn't work. The inspiration came from field research, where I noticed that the most engaged users interacted with the app very differently than the disengaged cohort.

Identifying User Behavior Patterns

We began by defining an "engaged" user vs. a "disengaged" user. Looking across the entire user base, we found that the most active quartile had a median login rate of 31.7 times a month, whereas the least active quartile had a median login rate of 4.9 times per month. We used the top quartile as "highly engaged" and the bottom quartile as "highly disengaged."

Comparing User Behavior

We compared the user behavior of highly engaged users to highly disengaged users, looking at the last 30 days. Surprisingly, we found that highly engaged users regularly reassigned leads to teammates, rejected leads, and set vacations within the app. Highly disengaged users were far more likely to give in-app feedback.

The Power Users

We were stunned by the results. We thought that the most engaged (and successful) agents would hold onto as many leads as possible, use our prioritization feature to order their pipeline by priority, and rarely take vacations. The data proved these assumptions completely wrong.

In reality, our most engaged agents rarely used our prioritization feature, instead frequently rejecting leads and reassigned them to teammates. They also regularly set vacations in the app.

Understanding What Makes an Engaged User

Confused by these results, we turned to qualitative user research to find the root cause behind the behaviors we saw in our analysis. Through 1:1 interviews with highly engaged and highly disengaged users, we uncovered the following insights:

  • Highly engaged users often rejected leads to focus on delivering the best possible experience for their clients.
  • They reassigned leads to teammates who specialized in a specific niche.
  • They didn't use our "set lead priority" and "sort pipeline by priority" features because priorities changed frequently.
  • They regularly set vacations in the app, which automatically rejected all inbound leads while they were on vacation.

The Takeaway

By combining user analytics with qualitative feedback, we uncovered the true reasons behind our users' behavior. This newfound understanding enabled us to drive product changes that supercharged user engagement and reduced churn.