Using web funnel analytics to recover churn and boost ltv

After we moved the paywall to the web I gained far richer signals about who converted and why. We could see creative-level differences in downstream behavior and build win-back campaigns tied to the original web order.

What worked for us:

  • capture emails at checkout so you can run targeted win-back and refund offers without relying on in‑app messaging.
  • feed web funnel events into your analytics to find downstream signals that predict retention.
  • offer web-only win-back promos and restore access in-app after they redeem, then track LTV improvements.

These plays improved our ability to recover revenue and ultimately lifted LTV. Anyone tried running win-back flows from web orders to re-enable in-app access?

I started capturing email at checkout and it changed our win-back game.

We sent targeted offers to people who converted but didn’t stick. When they redeemed we restored access in the app. The setup required simple mapping code and a hook into our subscription sync.

Web funnel analytics gave us the signals we were missing. We could spot which creatives produced high churn and which ones produced sticky users.

I then ran win-back offers to specific cohorts and restored entitlements after purchase. That closed a big LTV gap for us.

email win back worked better than I expected

Use the web funnel to classify cohorts by early behavior and creative source. Then run tailored win-back campaigns for high-value cohorts and track LTV uplift.

Operationally you need: email capture, a redemption flow that hits your subscription backend, and a sync so the app recognizes the restored entitlement. This approach is cheaper than generic blast promotions and improves retention predictably.

We automated refunds and targeted churned users with a small discount via email. When they repurchased we pushed the entitlement back to the app and tracked their 30 day retention. It improved our recovery rate noticeably.

Web analytics helped us see who was worth chasing.

We focused win-back on cohorts with higher initial usage and it paid off.