Been digging into our user data and realized we’re just looking backwards at what happened.
Want to start predicting which users will churn or upgrade before it actually happens.
Anyone moved from descriptive to predictive analytics? What worked for you?
We track users who stop opening the app daily. Usually means they’ll churn in two weeks.
Support tickets spike right before people cancel their subscriptions.
Payment behavior tells you everything about future actions. Users who switch from annual to monthly billing are basically waving a red flag.
For upgrades, watch when people export data or use sharing features more than usual. They’re hitting growth points in their business.
I set up simple email alerts when these patterns happen instead of waiting for monthly reports. Lets me reach out while the moment is still hot.
Built a simple scoring system for a subscription app using just 3 metrics: login frequency, feature usage depth, and payment method changes.
Users who scored below 30 had an 80% churn rate within 30 days. We caught them early and ran targeted retention campaigns.
The upgrade side was trickier. Found that users who hit usage limits 3+ times in a week were 5x more likely to upgrade. Set up push notifications for those moments.
Skipped the fancy algorithms and used basic IF/THEN rules in our CRM. Worked better than expected and took 2 weeks to set up instead of months.
Start with cohort analysis to spot patterns in user behavior before they churn or upgrade. Look at actions users take 7-14 days before churning. Usually it’s decreased session frequency or skipping key features. For upgrades, track engagement spikes with premium features during trials. Once you map these signals, set up automated triggers. Send retention campaigns when users hit churn warning signs or upgrade prompts when they show buying behavior. Don’t overcomplicate it with fancy ML models yet. Simple behavioral triggers work better than complex predictions.