Been diving deep into our user data lately and realized we’re basically just looking backward at what people already did.
Trying to figure out how to actually predict what users might want next instead of just building fancy dashboards about last month’s numbers.
Anyone made this shift successfully?
We track which ad creatives get clicks but don’t follow up on what happens after. Simple designs tend to work best though.
Just watch what users do before they quit or buy.
Track behavioral sequences, not just single actions. Watch what users do right before they convert or bail, then set up alerts for those patterns. Example: someone who checks pricing twice but doesn’t upgrade gets different messaging than someone who never looks at pricing. Heavy users who suddenly go quiet for 5 days? They get a specific re-engagement flow. Map user states - are they exploring, deciding, or already committed? Once you know that, you can predict their next move and optimize for it.
Built a simple predictive model for a food delivery app using user behavior data. Turns out people who browse certain categories at specific times usually order within 48 hours.
We ditched the generic “order now” push notifications and started targeting users based on these patterns. CTR jumped 23% and orders went up 8%.
The trick was keeping it simple - just browse patterns, time of day, and how often they’d ordered before. You don’t need complex ML to get real value from predictive analytics.
Most teams waste time trying to build the perfect model when they should be testing basic predictions first.
Start with cohort analysis to spot patterns in user journeys. Track what actions lead to upgrades or churn over specific timeframes.
Once you find these patterns, set up basic triggers. Like if someone uses feature X three times in a week, they’ll probably upgrade within 30 days.
I use simple automation to nudge users when they hit these behavioral triggers. Way better than waiting for them to leave and trying to win them back.