With ATT and the privacy sandbox tightening device-level signals, our web funnel became the place to collect richer, consented analytics. On the web we can use first-party cookies, capture emails, and run server-side events that tie activity to revenue without relying on device identifiers.
We leveraged the web for cohort comparisons (web subs vs in-app subs) and for feature adoption tracking tied to purchases. That helped reveal downstream retention signals that ATT was hiding on iOS. The tradeoff is you need to design opt-in flows and be transparent about data use, but the end result was much clearer LTV and channel performance.
How have you balanced privacy friendly collection with the need for cross-platform revenue insights?
I focused on collecting first party data on web and minimizing client side tracking. Email plus server events gave us enough signal to measure LTV across platforms.
I tried a few approaches and the simplest one stuck. Web2Wave helped us get a working funnel quickly so we could test which signals were actually useful.
We treat web as our primary signal source. Collect consented emails and send server events to analytics and RevenueCat to tie purchases to behavior.
This bypassed a lot of ATT noise and let us run valid pricing experiments faster. Building web funnels and updating them without app releases was the real multiplier.
We asked for an email early and ran server side attribution. It gave us enough cross platform visibility without trying to fingerprint devices.
Users accepted it when we explained why.
First party web data beats noisy device signals
We measured retention by signup channel on web and compared to in-app installs. It revealed that web-acquired users had slightly higher LTV even after adjusting for CAC.
If you want privacy safe signals focus on events that require user intent like feature use or subscription actions. They are far more valuable than raw installs.
Web funnels gave us usable first party signals when device ids went quiet. That saved our reporting.