Does CustomerLabs write to BigQuery in real time?

Yes. CustomerLabs writes events, audience membership, identity-resolved profiles, and offline conversions to BigQuery in real time through the BigQuery streaming API. Looker Studio dashboards, BI reports, and SQL queries always reflect the latest state.

Can I build attribution reports in Looker Studio using BigQuery data from CustomerLabs?

Yes. CustomerLabs writes identity-stitched website events, CRM stage transitions, offline conversions, and ad click context (GCLID, fbclid) to BigQuery, all joined on one CustomerLabs ID. Build first-click, last-click, multi-touch, channel-wise, geo-wise, and state-wise attribution reports in Looker Studio without manual data stitching.

Can ecommerce brands build POAS and discount profit reports in BigQuery?

Yes. CustomerLabs writes enriched ecommerce events to BigQuery including new vs returning customer flags, discount vs non-discount revenue, gross margin, POAS, AOV by cohort, and category-level ROAS. Build custom revenue reports in Looker Studio that GA4 and Shopify don't support out of the box, with no template lock-in.

Can data science teams build predictive models on CustomerLabs data in BigQuery?

Yes. CustomerLabs writes 100% accurate first-party data to BigQuery: identity-resolved events, CRM-enriched profiles, offline conversions, and audience membership history, all tied to one stable CustomerLabs ID. Churn models, LTV predictions, and propensity scores train on complete customer data, not behavioral fragments.

How does identity resolution work between CustomerLabs and BigQuery?

CustomerLabs runs identity resolution across browser, server, CRM, and offline sources to stitch email, phone, GCLID, fbclid, and device fingerprint into one profile. The resolved profile gets written to BigQuery with a stable CustomerLabs ID, so every row maps to one real customer. Subsequent updates merge into the same profile, not duplicate rows.

Does CustomerLabs work with GA4 BigQuery exports?

Yes. CustomerLabs writes identity-stitched events and CRM-enriched profiles to BigQuery in tables that join cleanly with your GA4 BigQuery export on CustomerLabs ID or hashed email. Query one warehouse for raw GA4 events plus identity-resolved CustomerLabs events, building enriched reports GA4 export alone can't support.

What BigQuery permissions does CustomerLabs need?

CustomerLabs needs BigQuery Data Editor permissions on the destination datasets to write events, profiles, and audiences. BigQuery Job User permissions are required to run insert and streaming operations. CustomerLabs operates within the permissions granted to the authenticated service account, with full audit logging.

Can I export offline conversions and CRM stages to BigQuery for revenue reporting?

Yes. CustomerLabs writes offline conversion events with attributed click context (GCLID, fbclid, campaign source), CRM stage values, and revenue records to BigQuery. Revenue teams build real-time revenue dashboards by campaign, by channel, by region, and by lifecycle stage in Looker Studio.

Should I use BigQuery, Google Sheets, or CSV Upload as my export destination?

BigQuery fits analytics-heavy data warehousing, attribution reports, predictive modeling, and high-volume real-time exports. Google Sheets fits ops workflows, ad-hoc reporting, and live dashboards for non-technical teams. CSV Upload fits scheduled exports, historical backfills, and partner reports. Most brands use all three for different use cases.

BigQuery Integration with CustomerLabs

CustomerLabs streams identity-resolved events, CRM profiles, and offline conversions into BigQuery in real time — one clean source of truth for attribution.

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How brands use CustomerLabs for BigQuery

Lead Gen Revenue Teams Build Attribution And Revenue Reports Across CRM, Web, And Ad Platforms

Lead gen sales cycles run long and span CRM, website, ad platforms, and offline touchpoints. Revenue teams need to see where pipeline comes from, which channels close, and which campaigns deserve credit, but the data sits in 4 to 6 separate systems. CustomerLabs pulls store, CRM, and offline business data in as sources, so the 4 to 6 systems become one feed before anything reaches the warehouse.
CustomerLabs writes identity-stitched events, CRM stage transitions, offline conversions, ad click context (GCLID, fbclid), and revenue values to BigQuery in real time, all joined on one CustomerLabs ID per buyer.
Revenue teams build attribution reports in Looker Studio (first-click, last-click, multi-touch, channel-wise, geo-wise, state-wise) using one clean source. Where pipeline is generated, where it closes, and which campaigns deserve the budget become answerable questions, not estimates.

Ecommerce Brands Build Advanced Revenue Reports That GA4 And Shopify Can’t

GA4 doesn’t support advanced customer events like new vs returning customer ROAS, POAS, or post-purchase profitability calculations. Shopify only shows order data with limited reporting templates. Third-party attribution tools lock you into rigid templates that don’t fit your business.
CustomerLabs writes enriched ecommerce events to BigQuery including new vs returning customer flags, discount vs non-discount revenue, gross margin, POAS, AOV by cohort, category-level ROAS, and lifetime value per customer segment.
Ecommerce brands build fully custom revenue reports in Looker Studio — profit on ad spend after discounts, ROAS by new customer vs repeat buyer, category-level margin analysis, and POAS calculations all run on first-party data with no template lock-in.

Data Science Teams Build Models On 100% Accurate First-Party Data In BigQuery

Predictive models, churn analysis, LTV prediction, and propensity scoring need complete, identity-resolved customer data, but most data science teams train on incomplete events from GA4, partial CRM exports, and fragmented behavioral data with missing identifiers.
CustomerLabs writes 100% accurate first-party data to BigQuery: identity-resolved events, CRM-enriched profiles, offline conversions, audience membership history, and revenue values, all tied to one stable CustomerLabs ID per customer.
Data science teams build models on complete data, not behavioral fragments. Churn models, LTV predictions, and propensity scores train on full customer history including offline events — anyone on the team can do it, not just technical specialists, because the data layer is already clean.

View the BigQuery Integration document with CustomerLabs

It allows me to integrate and seamlessly sync my customer data in real-time with my existing marketing tools like Google Analytics, Google Adwords, Mixpanel, Salesforce, and other tools. It also allows me to easily track, identify, segment, and analyze customer data. The platform allows me to manage personalized campaigns across channels without relying on my developers.

Ayako Y. Chief Marketing Officer, Marketing & Advertising

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BigQuery v2 Integration

destinations / data-warehouses / bigquery-v2

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BigQuery v1 Integration (Deprecated)

destinations / data-warehouses / bigquery

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How does CustomerLabs send data to BigQuery?

CustomerLabs connects to BigQuery through Google Cloud authentication using a service account or OAuth. Authenticate your Google Cloud project in CustomerLabs, select the destination BigQuery dataset and tables, and CustomerLabs streams events, profiles, and audiences via the BigQuery streaming API in real time. No code required.

How does CustomerLabs send data to BigQuery?

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