Tried several churn prediction tools recently, but they all seem designed for larger companies with tons of data.
We only have about 800 active users, and the predictions are often off.
Considering building something custom, but not sure if that’s too much for now.
Custom build is overkill at 800 users. You can spot churn patterns manually at that size.
Look at basic metrics first. Track days since last login, feature usage drops, and support ticket patterns. Put those in a simple spreadsheet.
Most churn prediction tools need thousands of users to work properly. You’re better off focusing on user interviews to understand why people leave.
Save the fancy software for when you hit 5k+ users.
Been in your exact spot with a meditation app we launched two years ago. Had around 600 users and wasted weeks testing Mixpanel’s prediction features.
What actually worked was setting up basic alerts in our analytics. We flagged users who didn’t open the app for 3 days, then sent a simple push notification.
Recovered about 20% of those users just from that basic trigger. Way better results than any prediction algorithm gave us.
The real insight came from reaching out to churned users directly. Turns out most left because our onboarding was confusing, not because we failed to predict they’d leave.
Fix your retention problems first, then worry about predicting them later.
Most prediction tools need way more data than you have. Focus on fixing obvious issues first.
With 800 users you can actually track churn better than most tools will predict it.
I just watch user behavior in my analytics and look for patterns like people who stop using key features or skip onboarding steps.
Simple cohort analysis in Google Sheets shows me more useful stuff than any prediction software ever did. You can see exactly when people drop off and what they were doing before that happened.
Most of these tools are built for companies with way more data points than you have right now.
Just track login frequency and feature usage manually right now.