Can built-in web refund and win-back flows give useful event signals for improving ltv?

We added simple win-back offers and a flexible refund workflow on the web funnel to test how users respond after they cancel. The workflows logged events like ‘winback_offer_shown’, ‘winback_clicked’, and ‘refund_requested’ with the original UTM attached. That gave us direct signals about which campaigns produced churn-prone users and which offers could win them back.

From that data we adjusted early offers and trial lengths. The surprising part was how many users who requested refunds accepted a small credit or discounted month instead. Logging those interactions without touching the app made the experiments fast and measurable.

Has anyone used web-level refund events to change onboarding or pricing?

We implemented a win-back flow that offered a one month discount and logged the outcome.

It let us see which channels had higher refund rates and which offers recovered users.

You can make quick changes to the web flow and measure impact the same day.

Yes. Web refund and win-back events fed directly into our channel reports and showed which campaigns needed different onboarding.

We tested a small credit vs a trial extension and measured LTV differences quickly.

We found win-backs worked best right after cancellation.

Quick, targeted offers recovered the users who were on the fence.

Logs made it obvious which campaigns brought those users.

Winbacks worked well

Refunds taught us who to target

Treat refunds and win-backs as testable features. Track the exact campaign and offer shown when the user canceled and when the win-back was presented. Measure not just immediate reactivation but a three month retention window to understand real LTV impact. Often a cheap win-back gives a short bump but not long term retention. Use the web to iterate offers fast and then push the best ones into broader tests.

Our refund event revealed one ad group with very high refund rate.

We paused that creative and shifted spend. Revenue went up without extra dev work.

We saw clear signals from refund events.

They helped identify low quality traffic quickly.