CONNECTIONS / MongoDB / MongoDB to Google Customer Match
From MongoDB collections to Google Customer Match.
Signals reads your MongoDB collections through a read-only database user scoped to the MongoDB collections you name, then projects your segments into Google Ads as Customer Match lists so Google Ads sees match-ready audiences without an export.
- ● mongodb → google.match :: live
- > read segments rows=57,524
- hash sha256(email,phone) consent=filtered
- route google deliver
- ✓ delivered · 46,594 matched
WHAT THIS ENABLES
MongoDB to Google Customer Match
MongoDB to Google Customer Match: Signals reads your MongoDB collections and projects your segments into Google Ads as Customer Match lists, hashed and consent-checked each time the read runs.
- Signals projects your segments into Google Ads as Customer Match lists, reading your MongoDB collections each time the read runs.
- Lists that self-maintain across syncs, with no export step out of your MongoDB collections.
WHAT FLOWS WHERE
MongoDB to Customer Match, mapped.
Each sync hands the customer segments from your MongoDB collections to Signals, which hashes and filters every record, then Google Ads' Customer Match API logs the match-ready audiences for the serving-size threshold.
Each sync hands the customer segments from your MongoDB collections to Signals, which hashes and filters every record, then Google Ads' Customer Match API logs the match-ready audiences for the serving-size threshold.
Built for the teams that own the number.
Application teams running MongoDB who need Customer Match lists that add joiners and drop lapsed members each sync out of MongoDB collections.
From kickoff to verified events.
-
Connect
A read-only database user scoped to the MongoDB collections you name, mapped to Google Ads' Customer Match API and tuned for the serving-size threshold.
-
Map
Columns from your MongoDB collections align to Google Ads' Customer Match API in the visual mapper, hashed as the read runs and checked for the serving-size threshold.
-
Deliver
Each time the read runs, Signals reads your MongoDB collections and projects your segments into Google Ads as Customer Match lists, reads only the records changed since the last run, with the serving-size threshold watched in the debugger.
What ships with this use case.
Customer Match lists that add joiners and drop lapsed members each sync from your MongoDB collections, delivered each time the read runs.
Lists that self-maintain across syncs out of your MongoDB collections, delivered each time the read runs without read load on the database climbing.
Match feedback on the serving-size threshold from Google Ads' Customer Match API each run, tied back to your MongoDB collections.
What Customer Match actually receives.
- ● signals :: audience upload
- > POST /user_list_operations source=mongodb.segments
- operation ADD · user_list "segment_64772"
- hashed_email sha256 "b02cf1…" · hashed_phone sha256 "3ad990…"
- members_uploaded 57,524 · members_matched 46,594
- ✓ accepted match=81%
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How often does the MongoDB Google Customer Match sync run?
The pace follows your MongoDB collections: Signals sends changes from MongoDB to Google Ads each time the read runs. Because it reads only the records changed since the last run, Customer Match lists that add joiners and drop lapsed members each sync stays current while application teams running MongoDB keep the serving-size threshold in view. Delivery needs no export and no manual upload.
What match rate should a MongoDB-sourced Google Customer Match batch expect?
In MongoDB, match rate tracks the email and phone quality your MongoDB collections carry. Clean identifiers land for Google Ads; blank ones never will. The first Customer Match API report shows application teams running MongoDB where the serving-size threshold needs work, a data-quality task handled in MongoDB.
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