What App Store experiments measure that paywall A/B tests can't

Subscription apps use RevenueCat or Adapty for paywall A/B tests. Those test in-app paywalls — what the user sees after install. App Store-level price experiments test something fundamentally different: who installs in the first place.

The mature subscription-app stack in 2026 includes a paywall A/B testing tool: RevenueCat's Experiments, Adapty's placement testing, Superwall's paywall builder. They share a workload: present different in-app paywalls to different user cohorts, measure which paywall converts better. They're excellent at this. They're also not the same thing as App Store-level price experiments.

What each tests

A paywall A/B test compares: User A sees paywall variant X, User B sees paywall variant Y. Both users have already installed the app. The test measures paywall-conversion (visitor → subscriber). It does not measure: did variant X bring more visitors in the first place?

An App Store-level price experiment compares: in Country Set A, the listing price is $9.99; in Country Set B, the listing price is $7.99. The test measures listing-level conversion (impression → install) AND eventual revenue. Whether the user even encountered your paywall depends on whether the listing price discouraged them at the App Store stage.

Why this distinction matters

Paywall A/B testing produces local optima — the best paywall design for the users you're already getting. App Store-level price testing produces global optima — the price at which the most-valuable mix of users install in the first place. Both are needed; they answer different questions.

The blind spot in most subscription stacks is the second one. RevenueCat doesn't test App Store-level prices because it can't — the API surface RevenueCat operates on (StoreKit) doesn't expose listing-price variants. App Store-level price A/B testing has to happen at the ASC API layer, which is a different architecture.

The pair-up

The mature setup is both layers: App Store-level price experiments to set the listing price per territory, paywall A/B tests to optimize the post-install conversion. They feed each other. Cheaper listing prices in emerging markets bring in more visitors; the paywall optimization decides what fraction of those visitors becomes subscribers.

What this means for app builders

If you only run paywall A/B tests, you're measuring downstream of a variable you haven't optimized. Set the listing price right first (per territory, PPP-adjusted), then optimize the paywall against the user mix that listing brings you. Doing them in the other order produces local optima that don't generalize.