Reading Apple Sales reports — five undervalued columns

Apple's daily Sales reports (the gzipped TSV pulled via the ASC API) contain 30+ columns. Most teams read four. The other 26 contain signal that's usually ignored — and a few are exactly what you need.

The Apple Sales report — the daily gzipped TSV every App Store Connect API integration can pull — has thirty-plus columns. Most analytics dashboards read four: Units, Developer Proceeds, Country Code, SKU. The remaining columns are mostly ignored. A few of them carry signal that's exactly what serious operators need.

Five columns worth reading

1. Promo Code — populated when a sale came through a promo code. Lets you track which promo campaign actually drove conversion vs which one inflated free-trial counts that churned. If you run codes and don't bucket by this column, you don't know what worked.

2. Preserved Pricing — boolean, set when a subscriber renewed at their grandfathered price after you raised the new-signup price. The ratio of preserved vs current-priced renewals tells you what fraction of your MRR is locked to historical pricing.

3. Customer Currency vs Currency of Proceeds — two columns. The first is what the user paid; the second is what hit your account after Apple's FX. The spread between them is where you find currency-conversion drag in markets where you're ostensibly priced fairly but the proceeds tell a different story.

4. Order Type — distinguishes new purchase, renewal, restored purchase. Restored purchases (re-installs of an app the user already owns) shouldn't inflate your "new users" count. They often do if you only read Units.

5. Device — granular, splits iPad / iPhone / Apple TV / Apple Watch / Vision Pro. Useful when your product runs across device classes and you want to know which is actually monetizing.

What this means for app builders

If your sales dashboard reads only Units + Proceeds + Country + SKU, you're missing the signal that distinguishes new users from re-installs, identifies preserved-pricing erosion, and bucket-tests promo campaigns. The data is already in the file you're pulling — start reading the columns you've been skipping.