Been diving deep into user behavior patterns in our app analytics.
Seeing some interesting signals that might help us predict what features users want before they actually ask for them.
Wondering if anyone else has cracked this code and how you approach it.
Built a prediction model for a productivity app tracking where users dropped off and their session patterns. Discovered users spending 3+ minutes on setup screens without finishing wanted simplified onboarding within 48 hours.
We pushed “quick setup” notifications to these users before they churned. Boosted retention by 8%.
The trick was watching micro-behaviors instead of just big actions - how long someone hovers over features, or repeatedly visiting screens without converting.
Focus on user complaints first and adapt quickly.
Don’t just track the main actions - watch what users do immediately after.
I’ve found that users who backtrack right after completing something usually didn’t get enough confirmation. Like when someone buys something but then hits the help section - they’re probably looking for order updates.
You’ll spot these patterns way before they actually contact support or write bad reviews.
Watch user behavior right before they hit up support or complain. That’s where the real problems are. I’ve worked with apps that track where users get stuck most, then build features to fix those exact spots. One client saw users constantly jumping between two screens, so they added split view. Revenue shot up 15%. Your analytics already have this stuff - just look for repetitive actions that waste time.
User needs change fast. Behaviors shift quickly, so it’s tough to predict what’s coming next.