Been diving deep into our email analytics and noticed some interesting patterns between open rates, click timing, and subsequent in-app actions.
Starting to wonder if there’s a way to use this data more strategically to predict which users are likely to churn or upgrade.
We built a simple scoring system that worked pretty well. Looked at open frequency over 30 days, plus whether people clicked within the first 6 hours of receiving emails.
Users who opened 3+ emails but never clicked were prime churn candidates. Hit them with retention campaigns.
The ones clicking fast but not converting in-app usually just needed a push notification reminder or different offer timing.
Don’t get too fancy with the data modeling. We tried machine learning stuff but basic behavioral triggers gave us 80% of the value with way less headache.
You can definitely use those patterns for predictions. Track users who click emails and take actions in your app.
I pay attention to the time gaps between clicks and purchases. Users engaging with emails but not converting can benefit from targeted messaging.
Connecting email data and app analytics will give you better insights.
Email timing can help but remember that app behavior can vary from email behavior.
Focus on engagement drop-offs rather than just opens and clicks.
Track users who went from regular email engagement to radio silence over 14 days. That’s your highest churn risk group.
For upgrades, look at users who click multiple emails in a short window but don’t convert. They’re interested but something is blocking them. Usually price sensitivity or feature confusion.
Set up automated triggers based on these patterns instead of trying to predict everything upfront. Much faster to implement and you’ll see results within weeks.
Just track who stops opening emails completely after week two.