Revenuecat Experiments

Setting up and managing experiments

A RevenueCat experiment compares two to four offerings: offering_a (the control) against offering_b–offering_d (the treatments). Enrolled customers are served their variant's offering instead of the project's current offering. Setting up an experiment means preparing one offering per variant, then creating the experiment as a draft.

Refer to the MCP tool schemas for the exact parameters of each tool; the create-experiment schema also lists the available experiment types with their recommended primary and secondary metrics.

Preparing the variants

Keep the variants identical except for the one aspect under test, otherwise results cannot be attributed to the change. For example, when measuring the impact of a lower subscription price, both variants should share the same paywall design and packages, differing only in the price of the products.

Control. When the control variant is "what production does today", pass the current offering (is_current in list-offerings) directly as offering_a_id — do not duplicate it. Duplicate only when control itself needs changes relative to production.

Treatments. First check with list-offerings whether an offering already exists that matches what the treatment should serve, and reuse it instead of duplicating. Otherwise, start each treatment from a duplicate of the offering closest to it (usually the control offering). duplicate-offering copies the packages (attaching the same existing products) and, if the source offering has a paywall, also copies it as a new unpublished draft; it returns the new offering, including its new paywall_id. Then apply the one change under test:

• Paywall change (copy, layout, CTA, pricing display, ...): this requires the app to use RevenueCat Paywalls — a paywall is an optional property of an offering, and if the control offering has none (paywall_id is null). Otherwise, edit the duplicated paywall with edit-paywall-ai. Request only the minimum changes required to measure the desired effect, and verify the result by looking at the resulting paywall.
• Price, trial, or introductory-offer change: these live on store products, so the treatment offering needs different products. Check with list-products whether suitable products already exist; create only the missing ones and their store counterparts (see the revenuecat-store-state skill). Then swap them into the duplicated offering's packages: detach the copied products with detach-products-from-package, then attach the new ones with attach-products-to-package.
• Brand-new paywall: duplicate the offering with include_paywall: false, then call create-paywall-ai with the new offering's offering_id so the generated draft is attached to it directly. A paywall and an offering pair 1:1 — attach-offering-to-paywall fails if either side is already paired; undo a wrong pairing with detach-offering-from-paywall (unpublish the paywall first if it is published).

Publish treatment paywalls. A duplicated or newly created paywall is an unpublished draft, and a variant only serves the published version. If new paywalls were created in previous steps, publish each treatment paywall with publish-paywall before the experiment starts. This is the exception to the rule against proactive publishing, and it is safe: the treatment offering is not the current offering, so no customer sees the paywall until the experiment starts enrolling.

Creating the experiment

Create a draft with create-experiment: display name, enrollment_percentage, the variant offering IDs, experiment_type, a primary_metric (plus secondary_metrics) matching the hypothesis, enrollment_mode (only_new unless the user explicitly wants to include existing customers), and any targeting and notes. Creating never starts the experiment. Adjust a draft with update-experiment.

Before starting, verify each variant: treatment paywalls are published, packages contain the intended products, and any new products are available on the stores.

Lifecycle

• start-experiment begins enrolling real customers — only run it with the user's explicit confirmation.
• pause-experiment stops enrollment but keeps serving enrolled customers their variant and keeps collecting data.
• resume-experiment continues enrollment.
• stop-experiment is terminal: enrolled customers fall back to the default offering and the experiment can never be restarted.

To interpret results once the experiment is running, use the revenuecat-experiment-analysis skill.