Best Practices

Recommended setup

Start with a focused entity

Don't pull all entities at once. Start with the one most relevant to your goal — Reports revenues for finance, Reports sales for growth tracking — then add others as needed.

Use Group by: date for time-series analysis

The default grouping is by product, which collapses all dates into totals. Switch to date if you want to build trend charts or track changes over time.

Set your store parameter explicitly

The search_store parameter defaults to Apple. If you track apps on Google Play or other stores, set this explicitly so you don't accidentally miss data.

Join revenue and ad spend data

Use Coupler.io's Join transformation to merge Reports revenues and Reports adspends on the product + date dimensions. This gives you a single table to calculate ROAS without manual spreadsheet work.

Data refresh and scheduling

Account for store reporting delays

Apple and Google typically finalize data 24–72 hours after the day ends. Schedule refreshes for mid-morning (rather than midnight) to reduce the chance of pulling incomplete day-of data.

Use Append for multi-account reporting

If you manage apps across multiple Appfigures accounts, create separate data flows for each and use the Append transformation to combine them into one destination sheet or table.

Performance optimization

Limit date ranges for large catalogs

If you have many apps, pulling a full year of daily data in one run can be slow or hit rate limits. Use a rolling 30–90 day window and store historical data incrementally.

Use the Statuses entity as a health check

Before debugging missing data in your reports, pull the Statuses entity first. It quickly tells you whether Appfigures has successfully synced each connected store — saving time on unnecessary troubleshooting.

Common pitfalls

Don't compare proceeds to gross revenue. Appfigures reports both, and mixing them up leads to significant revenue discrepancies — especially when comparing against store dashboards that show gross figures.

Do

• Set group_by to date when building time-series dashboards

• Verify the search_store parameter matches your target store

• Check the Statuses entity when data looks incomplete

• Join revenue + ad spend data at the product level for ROI analysis

Don't

• Pull all 11 entities in a single data flow without a clear use case

• Use the default group_by: product setting when you need daily trends

• Assume today's data is final — store reporting lags by 24–72 hours

• Mix proceeds and gross revenue metrics in the same calculation