CONNECTIONS / BigQuery / BigQuery to Google Store Sales
From BigQuery datasets to Google Store Sales matching.
Point Signals at the dataset behind a read-only service account, and it matches in-person transactions to Google accounts as store-sales conversions on the cadence your scheduled query already keeps.
- ● bigquery → google.sales :: live
- > read sales.pos_transactions rows=29,867
- hash sha256(email,phone) consent=filtered
- route google deliver
- ✓ delivered · 23,894 matched
WHAT THIS ENABLES
BigQuery to Google Store Sales
BigQuery to Google Store Sales: A scheduled query reads the offline and in-store sales in your BigQuery tables, and Signals matches in-person transactions to Google accounts as store-sales conversions, hashed and consent-checked whenever the scheduled query reruns.
- Signals matches in-person transactions to Google accounts as store-sales conversions, reading your BigQuery tables whenever the scheduled query reruns.
- Counter and showroom revenue inside the same ROAS view as online orders, fed by analytics engineers running BigQuery scheduled queries.
The click, the conversion, and the credit.
The conversion happens off Google, away from any pixel. Here is how BigQuery closes the loop.
Built for the teams that own the number.
Analytics engineers running BigQuery scheduled queries who need counter and showroom revenue inside the same ROAS view as online orders.
From kickoff to verified events.
-
Connect
A read-only service account with dataViewer on the one dataset you nominate to feed Google Ads' Store Sales Direct API, sized for the store-sales match rate.
-
Map
A scheduled query reads your BigQuery tables, and its columns map to Google Ads' Store Sales Direct API in the visual mapper, hashed and checked for the store-sales match rate.
-
Deliver
Whenever the scheduled query reruns, Signals matches in-person transactions to Google accounts as store-sales conversions, scans only the rows the query changed, with the store-sales match rate watched in the debugger.
What changes when the CSV goes away.
| Capability | Manual CSV upload | Datahash |
|---|---|---|
| Reporting | Offline sales sit in a separate export, reconciled by hand. | Revenue counted in the same Store Sales reporting as web conversions. |
| Deduplication | A re-uploaded file risks counting the same conversion twice. | Deduped delivery, so a resent record never counts as a second conversion. |
| Match visibility | Match quality is a guess until the numbers look off. | Match rate reported per upload, tied to the source event set that produced it. |
| Effort and latency | An analyst exports and uploads on a manual cadence. | Server-side and automatic the moment BigQuery records the event. |
What Store Sales actually receives.
- ● signals :: store sales upload
- > POST /offline_user_data_jobs source=bigquery.sales.pos_transactions
- transaction_time 2026-07-18T01:47:54 · transaction_amount_micros 22797000000
- currency_code GBP
- hashed_email sha256 "6d2e91…" · hashed_last_name sha256 "cc410a…"
- ✓ accepted match=80%
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How often does the BigQuery Google Store Sales sync run?
The scheduled query sets the pace: rebuild the offline and in-store sales nightly and delivery to Google Ads is nightly, or refresh hourly for an hourly feed. Each run only reads the rows the query changed, so counter and showroom revenue inside the same ROAS view as online orders stays current for the store-sales match rate while BigQuery slot cost barely moves.
What match rate should a BigQuery-sourced Google Store Sales batch expect?
Coverage in your BigQuery tables decides the rate, not BigQuery. A row with a current hashed email or phone matches; one missing both cannot, however the query is tuned. The first run's report from Google Ads' Store Sales Direct API sets a baseline, the number omnichannel retailers reconciling point-of-sale against Google spend weigh against the store-sales match rate.
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