Most analytics just tell you what users already did. I want to actually predict who’s likely to churn or upgrade next week.
Anyone moved beyond basic reporting into real predictive stuff? What actually worked for you?
We just look at support ticket volume. Users who contact support twice in a month usually churn.
Tracking specific user actions can help a lot. For example, look at how often they visit settings or pricing pages.
After gathering enough data, patterns emerge. Users visiting the upgrade page multiple times often convert if you send a follow-up email with an offer.
For predicting churn, watch for engagement drops over a week. If they stop using main features, they tend to cancel soon.
Built a simple model for a subscription app that used login frequency + feature usage depth. Not fancy machine learning, just basic scoring.
Users who dropped below 3 logins per week AND stopped using the core feature were 73% likely to churn within 14 days. We’d trigger a retention email series when they hit that threshold.
For upgrades, we tracked trial users who exported data or hit usage limits. Those users converted at 4x the rate when we reached out within 24 hours.
The key was finding 2-3 strong signals instead of trying to track everything. Most predictive stuff fails because people overcomplicate it.
Started with simple cohort analysis then added user scoring. Works fine.
Create segments based on how fast users behave, not just the numbers. Track how quickly they move through your funnel. A user who upgrades their profile and invites teammates in 48 hours shows more intent than someone who takes 3 weeks to do the same. I use time-based scoring. Fast adopters get upgrade prompts while slow ones receive educational content. For predicting churn, focus on drops in usage consistency instead of total usage. A user going from daily use to every 3 days is a bigger concern than someone using it consistently twice a week.