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    CONNECTIONS / BigQuery / BigQuery to LinkedIn Offline CAPI

    From BigQuery datasets to LinkedIn account-revenue reporting.

    Point Signals at the dataset behind a read-only service account, and it reports closed B2B deals so LinkedIn optimizes on account revenue on the cadence your scheduled query already keeps.

    Talk to our team Explore the setup

    bigquery -> linkedin-offline-capi :: live

    1. ● bigquery → linkedin.capi :: live
    2. > read sales.pos_transactions rows=14,573
    3. hash sha256(email,phone) consent=filtered
    4. route linkedin deliver
    5. ✓ delivered · 12,533 matched

    WHAT THIS ENABLES

    BigQuery to LinkedIn Offline CAPI

    BigQuery to LinkedIn Offline CAPI: A scheduled query reads the offline and in-store sales in your BigQuery tables, and Signals reports closed B2B deals so LinkedIn optimizes on account revenue, hashed and consent-checked whenever the scheduled query reruns.

    • Signals reports closed B2B deals so LinkedIn optimizes on account revenue, reading your BigQuery tables whenever the scheduled query reruns.
    • Pipeline revenue tied to the LinkedIn campaigns that sourced it, fed by analytics engineers running BigQuery scheduled queries.
    WHAT FLOWS WHERE

    The click, the conversion, and the credit.

    The conversion happens off LinkedIn, away from any pixel. Here is how BigQuery closes the loop.

    LinkedIn credits the campaign that sourced the conversion conversion happens off-platform Ad click on LinkedIn Scheduled query sync recorded in BigQuery S Signals hash · dedupe Conversions API delivered · credited
    WHO THIS IS FOR

    Built for the teams that own the number.

    B2B teams whose deals close long after the impression, with the numbers already modeled in BigQuery tables and GA4 exports.

    Analytics engineers running BigQuery scheduled queries who need pipeline revenue tied to the LinkedIn campaigns that sourced it.

    HOW DATAHASH SETS IT UP

    From kickoff to verified events.

    1. Connect

      A read-only service account with dataViewer on the one dataset you nominate to feed LinkedIn's Conversions API, sized for long B2B attribution windows.

    2. Map

      A scheduled query reads your BigQuery tables, and its columns map to LinkedIn's Conversions API in the visual mapper, hashed and checked for long B2B attribution windows.

    3. Deliver

      Whenever the scheduled query reruns, Signals reports closed B2B deals so LinkedIn optimizes on account revenue, scans only the rows the query changed, with long B2B attribution windows watched in the debugger.

    WHAT YOU GET

    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.
    ON THE WIRE

    What Offline CAPI actually receives.

    signals :: bigquery.linkedin-offline-capi :: wire

    1. ● signals :: event payload
    2. > POST /conversionEvents source=bigquery.sales.pos_transactions
    3. conversion "urn:lla:llaPartnerConversion:57596"
    4. conversionHappenedAt 1784056596000
    5. user.userIds [{ idType: "SHA256_EMAIL", idValue: "9c41af…" }]
    6. ✓ accepted · conversionValue 26162 USD
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    FAQ

    Asked on almost every call.

    How often does the BigQuery LinkedIn Offline CAPI sync run?

    The scheduled query sets the pace: rebuild the offline and in-store sales nightly and delivery to LinkedIn is nightly, or refresh hourly for an hourly feed. Each run only reads the rows the query changed, so pipeline revenue tied to the LinkedIn campaigns that sourced it stays current for long B2B attribution windows while BigQuery slot cost barely moves.

    What match rate should a BigQuery-sourced LinkedIn 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 LinkedIn's Conversions API sets a baseline, the number B2B teams whose deals close long after the impression weigh against long B2B attribution windows.

    NEXT STEP

    BigQuery to LinkedIn Offline CAPI, in production this week.

    Talk to our team All BigQuery use cases