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Business/2026-06-13Advanced

Getting to the Real Revenue Number — A Pipeline that Reconciles AdMob, App Store, Google Play, and Stripe

Dashboard revenue and the money that actually lands in your account do not match. Here is an aggregation pipeline that absorbs currency, timezone, and the gap between estimated and finalized figures across four revenue sources — with the implementation and operating judgment from running six apps.

revenue opsAdMob71App Store Connect13Stripe17data design

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One month AdMob's dashboard showed a 12% gain over the prior month, yet the actual payout was flat. I suspected a bug at first, but the cause was simple: the dashboard's "estimated revenue" and the finalized payout are different things. The moment FX settles, post-hoc invalid-traffic adjustments, reaching the payment threshold — several factors make the number on screen diverge from the number in the bank.

Running six apps as an indie developer, revenue arrives from four sources: AdMob, App Store, Google Play, and Stripe. Each has its own currency, its own timezone, its own notion of "estimated versus finalized." Add them up naively and the numbers stop matching somewhere every month. Here is the aggregation pipeline I built to reach the real number by reconciling all four — with the normalization schema, the daily aggregation, and the monthly balancing routine.

Why the numbers diverge — the character of each source

Before designing reconciliation, I had to understand how each of the four sources lies.

AdMob's screen shows estimated revenue. Invalid-traffic deductions and FX settlement pull the month-end finalized figure down by a few percent. In my own records, the gap between estimate and finalized stayed roughly in the 2–6% range. App Store Connect and Google Play have three layers that each differ: the on-screen "sales," the finalized figure in financial reports, and the actual payout net of fees. Stripe is comparatively honest, but refunds and chargebacks arrive later as negatives, so you have to keep occurrence date and settlement date separate or things stop matching across month boundaries.

In other words, no source tells you unambiguously "how much you earned right now." I decided from the start that the foundation of the design is keeping estimated and finalized as separate columns.

A normalization schema — reshape everything into one form

The four data formats are all different, so I normalize them into a common shape first. The schema I use is this.

CREATE TABLE revenue_events (
  app_id        TEXT NOT NULL,        -- one of the six
  source        TEXT NOT NULL,        -- admob / appstore / googleplay / stripe
  kind          TEXT NOT NULL,        -- ad / iap / subscription / refund
  occurred_on   DATE NOT NULL,        -- occurrence date (unified to UTC)
  amount_minor  INTEGER NOT NULL,     -- minor currency units (1 = 1 yen, or 1 cent)
  currency      TEXT NOT NULL,        -- JPY / USD etc.
  status        TEXT NOT NULL,        -- estimated / finalized
  fx_to_jpy     REAL,                 -- FX rate at finalization (NULL allowed for estimated)
  PRIMARY KEY (app_id, source, kind, occurred_on, status)
);

Three things matter. Storing amounts as integers in minor currency units (amount_minor) instead of floats prevents rounding error from accumulating. The status field cleanly separates estimated from finalized, and the primary key prevents duplicate rows for the same day and source. And the FX rate is saved as "the rate at finalization" in fx_to_jpy, not "the rate at aggregation time," so past numbers do not shift when you re-aggregate later.

If you convert currency at the aggregation-time rate by accident, the number you saw at the start of the month differs from the one at the end, and the dashboard becomes untrustworthy. I learned that the hard way once.

Thank you for reading this far.

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WHAT YOU'LL LEARN
Why estimates and finalized payouts diverge, broken down per source, and how to reconcile them
A normalization schema that absorbs currency, timezone, and refunds, plus a daily aggregation skeleton
A monthly routine that balances payouts against the books instead of trusting the dashboard at face value
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