| 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. |
| 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. |
| 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. |