Been diving into churn prediction models lately and honestly feeling a bit overwhelmed by all the different approaches.
Tried basic cohort analysis but it feels too reactive. Looking at machine learning options now but not sure which techniques actually move the needle for retention.
What’s been your experience with predictive models versus simpler approaches?
We use simple triggered emails when users miss a few days. Works better than expected.
Focus on user behavior instead of complex models.
Start with engagement scoring before jumping into ML. Track specific actions that predict churn - days since last login, feature usage drops, support tickets. Build a simple point system. Once you have that baseline working and driving retention actions, then layer on predictive models. Most apps I’ve worked with get 70% of the value from basic behavioral triggers. The fancy algorithms help but only after you nail the fundamentals.