Been working on churn prediction models for our app. Seeing some improvement in retention, but engagement rates aren’t budging much.
Anyone else found success with these models? Curious about what metrics you’re tracking and how you’re actioning the insights.
Yeah, we’ve been using churn prediction models for a while now. They definitely helped with retention, but engagement was trickier.
For metrics, we focused on:
The real game-changer was how we used the insights. Instead of just targeting high-risk users, we started tweaking the app experience based on churn likelihood.
For example, users likely to churn got more personalized content and gentler re-engagement nudges. We also adjusted difficulty levels in our game app to keep it challenging but not frustrating.
Engagement finally started climbing when we got more granular with our segments and tailored interventions. It’s an ongoing process though - what works changes pretty fast in this space.
Churn models are cool. But user interviews tell more. Talk to people who quit. Fix those problems.
Churn prediction is helpful, but it’s not a magic bullet for engagement. Here’s what actually moves the needle:
Metrics to watch: Daily active users, session length, and feature adoption rates.
Don’t overthink it. Focus on making your app genuinely useful and enjoyable. All the predictive models in the world can’t fix a product users don’t love.
Churn models are useful, but they’re just one piece of the puzzle.
I’ve found that combining them with in-app behavior tracking gives a fuller picture.
Focus on time spent in key features, completion rates for core tasks, and how often users return after notifications. Use these insights to tweak your app’s UX and feature set. It’s an ongoing process of adjusting and testing.
Churn models help a bit. We use them too. Try tweaking the app based on what you learn. Maybe add some fun stuff or make things easier for users who might leave.