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    CONNECTIONS / Redshift / Redshift to Google Customer Match

    Customer segments in Redshift, activated for Google Customer Match.

    Grant a scoped IAM role, name the table or materialized view, and Signals projects your segments into Google Ads as Customer Match lists with no UNLOAD to S3 and no file to hand off.

    Talk to our team Explore the setup

    redshift -> google-customer-match :: live

    1. ● redshift → google.match :: live
    2. > read marketing.high_value_customers rows=49,590
    3. hash sha256(email,phone) consent=filtered
    4. route google deliver
    5. ✓ delivered · 40,664 matched

    WHAT THIS ENABLES

    Redshift to Google Customer Match

    Redshift to Google Customer Match: The Redshift Data API reads the customer segments in your Redshift tables, and Signals projects your segments into Google Ads as Customer Match lists, hashed and consent-checked as the materialized view refreshes.

    • Signals projects your segments into Google Ads as Customer Match lists, reading your Redshift tables as the materialized view refreshes.
    • Customer Match lists that add joiners and drop lapsed members each sync, fed by analytics teams maintaining Redshift tables and materialized views.

    WHAT FLOWS WHERE

    Redshift to Customer Match, mapped.

    Signals pulls customer segments through the Redshift Data API, hashes and filters every row, and posts the match-ready audiences to Google Ads' Customer Match API, keeping the serving-size threshold in view.

    Signals pulls customer segments through the Redshift Data API, hashes and filters every row, and posts the match-ready audiences to Google Ads' Customer Match API, keeping the serving-size threshold in view.

    WHO THIS IS FOR

    Built for the teams that own the number.

    Growth teams feeding Google audiences from governed first-party data, with the numbers already modeled in Redshift tables and materialized views.

    Analytics teams maintaining Redshift tables and materialized views who need Customer Match lists that add joiners and drop lapsed members each sync.

    HOW DATAHASH SETS IT UP

    From kickoff to verified events.

    1. Connect

      A scoped IAM role with SELECT on the schema you name to feed Google Ads' Customer Match API, sized for the serving-size threshold.

    2. Map

      The Redshift Data API reads your Redshift tables, and its columns map to Google Ads' Customer Match API in the visual mapper, hashed and checked for the serving-size threshold.

    3. Deliver

      As the materialized view refreshes, Signals projects your segments into Google Ads as Customer Match lists, touches only the rows changed since the last read, with the serving-size threshold watched in the debugger.

    WHAT YOU GET

    What ships with this use case.

    Customer Match lists that add joiners and drop lapsed members each sync on every run, since Signals touches only the rows changed since the last read.

    Lists that self-maintain across syncs, delivered as the materialized view refreshes without RA3 load climbing.

    Each run returns a match report on the serving-size threshold from Google Ads' Customer Match API, read from your Redshift tables as the materialized view refreshes, at flat RA3 load.

    ON THE WIRE

    What Customer Match actually receives.

    signals :: redshift.google-customer-match :: wire

    1. ● signals :: audience upload
    2. > POST /user_list_operations source=redshift.marketing.high_value_customers
    3. operation ADD · user_list "segment_82753"
    4. hashed_email sha256 "b02cf1…" · hashed_phone sha256 "3ad990…"
    5. members_uploaded 49,590 · members_matched 40,664
    6. ✓ accepted match=82%
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    FAQ

    Asked on almost every call.

    How often does the Redshift Google Customer Match sync run?

    The refresh schedule drives it: when the materialized view rebuilds nightly, Google Ads gets a nightly feed, and an hourly refresh runs hourly. Each read touches only changed rows, so Customer Match lists that add joiners and drop lapsed members each sync stays fresh for the serving-size threshold without RA3 load rising.

    What match rate should a Redshift-sourced Google Customer Match batch expect?

    The fill rate of the view matters more than Redshift itself. A clean hashed email or phone matches; a blank one never will. The first scheduled read reports quality from Google Ads' Customer Match API, the figure growth teams feeding Google audiences from governed first-party data weigh against the serving-size threshold.

    NEXT STEP

    Redshift to Google Customer Match, in production this week.

    Talk to our team All Redshift use cases