CONNECTIONS / BigQuery / BigQuery to Snap Offline CAPI
From BigQuery datasets to Snapchat in-store conversions.
Point Signals at the dataset behind a read-only service account, and it reports in-store purchases against the Snapchat campaigns that reached the buyer on the cadence your scheduled query already keeps.
- ● bigquery → snap.capi :: live
- > read sales.pos_transactions rows=68,277
- hash sha256(email,phone) consent=filtered
- route snap deliver
- ✓ delivered · 57,353 matched
WHAT THIS ENABLES
BigQuery to Snapchat Offline CAPI
BigQuery to Snapchat Offline CAPI: A scheduled query reads the offline and in-store sales in your BigQuery tables, and Signals reports in-store purchases against the Snapchat campaigns that reached the buyer, hashed and consent-checked whenever the scheduled query reruns.
- Signals reports in-store purchases against the Snapchat campaigns that reached the buyer, reading your BigQuery tables whenever the scheduled query reruns.
- Physical-world sales counted inside Snapchat performance, fed by analytics engineers running BigQuery scheduled queries.
The click, the conversion, and the credit.
The conversion happens off Snap, 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 physical-world sales counted inside Snapchat performance.
From kickoff to verified events.
-
Connect
A read-only service account with dataViewer on the one dataset you nominate to feed Snapchat's Conversions API, sized for offline event timing.
-
Map
A scheduled query reads your BigQuery tables, and its columns map to Snapchat's Conversions API in the visual mapper, hashed and checked for offline event timing.
-
Deliver
Whenever the scheduled query reruns, Signals reports in-store purchases against the Snapchat campaigns that reached the buyer, scans only the rows the query changed, with offline event timing 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 Offline CAPI 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 Offline CAPI actually receives.
- ● signals :: event payload
- > POST /v2/conversion source=bigquery.sales.pos_transactions
- event_type "PURCHASE" · event_conversion_type "OFFLINE"
- hashed_email "5d8b22…" · hashed_phone_number "a179fe…"
- timestamp 1783976158 · price 19507 · currency GBP
- ✓ accepted · match=true
Adjacent moves on the same stack.
from BigQuery
BigQuery to Google OCI
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BigQuery to Google Store Sales
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ActiveCampaign to Snap Offline CAPI
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Snap Offline CAPI
Deliver offline conversions to Snap on a schedule that fits your operations.
Learn moreAsked on almost every call.
How often does the BigQuery Snap Offline CAPI sync run?
The scheduled query sets the pace: rebuild the offline and in-store sales nightly and delivery to Snapchat is nightly, or refresh hourly for an hourly feed. Each run only reads the rows the query changed, so physical-world sales counted inside Snapchat performance stays current for offline event timing while BigQuery slot cost barely moves.
What match rate should a BigQuery-sourced Snap Offline CAPI 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 Snapchat's Conversions API sets a baseline, the number retail and QSR advertisers whose Snapchat audience buys in person weigh against offline event timing.
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