Why app revenue numbers differ across dashboards

Write down what each number measures. A customer-spending total, developer proceeds, recurring revenue, and a payout can all appear beneath a heading that says revenue. They should not be expected to match simply because their labels look similar.

Make a comparison sheet with a column for each dashboard. Record its source, metric definition, period, currency, and filters. Unknown fields are useful findings: resolve those before trying to explain a percentage difference.

Apple documents differences caused by transaction processing and exchange rates across its reporting tools. Timezone choices can also move activity between dates. Comparing a fiscal period with a calendar month introduces another boundary difference.

Write the literal start and end dates in your comparison sheet. If possible, first compare amounts in the source currency. Once that matches, compare the conversion method as a separate step instead of changing the period and currency at the same time.

Check the latest complete report date in each system and look for incomplete imports. Apple publishes report availability and retention limits; an older date outside the available history cannot be recovered just by retrying a download.

In AppCradle, inspect the import status and missing reports for the store you are comparing. A daily refresh and a historical backfill serve different purposes. Record which history has been imported instead of assuming the account contains every past transaction.

RevenueCat documents that its charts use purchase data stored in RevenueCat. It identifies migration history, metric definitions, and currency or tax estimates as possible reasons for differences from store reports. Receipt coverage therefore matters when comparing that dataset with a report-based system.

For any provider, inspect its current definition of the metric you selected. Record how it handles refunds and later adjustments, then choose a small sample that both systems cover. Do not assume every tool has the same history or recognizes changes on the same date.

Start with the narrowest reproducible comparison. Once it matches, expand to another product, a longer period, and then a combined-store total. This makes it easier to identify the step that introduced the difference.

• Same store, app, product scope, and environment.
• Same metric definition, with fees and taxes treated consistently.
• Same date boundaries and an understood timezone.
• Same currency, or a documented conversion method.
• Refunds and adjustments interpreted using each source’s rules.
• Complete, non-duplicated report coverage for the chosen period.