Been trying to build something that predicts which users will churn but most models seem built for companies with tons of data.
With limited users and features, everything I’ve tested gives pretty useless results. What approaches have worked when you don’t have massive datasets?
Simple cohort analysis works better than fancy models when you have limited data.
Skip the complex models for now. I’ve been there with small datasets and the predictions are garbage.
What worked better was tracking simple behavioral signals. Like users who don’t log in within 48 hours of signup, or people who skip onboarding steps. Basic stuff but it actually flagged the right users.
For one B2B tool I worked on, we just looked at feature usage in the first week. Users who touched less than 3 core features churned 80% of the time. Simple rule but way more accurate than any ML model we tried.
Start with cohort analysis to spot patterns first. Once you see what behaviors matter, you can build basic rules around those.
I’d focus on getting more data before building anything fancy. The behavioral signals will tell you more than any algorithm right now.
Revenue per user in the first 30 days is what I track instead of complex models.
Users who don’t hit a certain spending threshold early on always churn eventually. You can set this up with basic analytics and it gives you clear numbers to work with.
Works even with 50-100 users because money talks louder than usage patterns.
Focus on engagement velocity instead of traditional churn models. Track how fast users complete key actions after signup, not just if they complete them. Users who take more than X days to reach their first value moment will churn. This works with small datasets because you’re measuring speed, not complex patterns. I’ve seen this catch churners 5-7 days before they actually leave, giving you time to intervene with targeted campaigns.
Just track login frequency. Works better than any model.