Cursive sends visitor data to BigQuery via streaming inserts or batch loads, enabling real-time visitor analytics at scale. Join visitor data with GA4 exports, Google Ads data, and product usage for complete cross-channel attribution and ML-powered lead scoring.
When Cursive identifies a visitor, here is exactly how that data maps into BigQuery. No manual entry, just clean, structured records.
| Cursive Field | BigQuery Field | What Happens |
|---|---|---|
| Identified Visitors | BigQuery Visitors Table | Each identified visitor is inserted into a BigQuery table with full visitor attributes. |
| Visitor Events | BigQuery Events Table (Streaming) | Real-time visitor events are streamed to BigQuery for immediate analysis. |
| Company Profiles | BigQuery Companies Dataset | Company data lands in a companies dataset for joining with visitor and CRM data. |
| Conversion Events | BigQuery Conversions Table | Conversion events (demo booked, deal closed) populate a conversions table for attribution analysis. |
Need a field that is not listed? Explore the Cursive platform to see every data point we capture.
Connecting Cursive with BigQuery unlocks workflows that save hours every week and make sure no qualified lead slips through the cracks.
Use BigQuery streaming inserts to analyze visitor data in near real-time. Build dashboards that show who is on your site right now, trending visitor companies, and real-time conversion rates. Query visitor data alongside GA4 BigQuery exports for unified analysis.
Use BigQuery ML to train lead scoring models directly in SQL. Feed Cursive visitor features (pages viewed, visit frequency, company size) alongside conversion outcomes to build models that predict which visitors are most likely to become customers.
Join Cursive visitor data with Google Ads, GA4, and CRM data in BigQuery to build a complete attribution model. Understand which channels drive the most identified visitors, which visitors convert, and what the true cost per qualified visitor is across channels.
With Cursive's visitor identification data flowing into BigQuery, you can build any workflow your revenue team needs.
Getting Cursive and BigQuery connected takes just a few minutes. Follow the steps below.
In Google Cloud Console, create a BigQuery dataset for Cursive data (e.g., cursive_visitors).
Create a service account with BigQuery Data Editor permissions and download the JSON key file.
In Cursive, go to Integrations > Add Destination and select BigQuery.
Upload the service account key file and specify the project ID, dataset, and table names.
Test the connection with a sample event, then verify data appears by running a SELECT query in the BigQuery console.
Need help getting set up? Our team can walk you through the entire connection process on a quick call and recommend the highest-impact automations for your stack.
Cursive supports both. Streaming inserts provide near real-time data availability (within seconds). Batch loads via Cloud Storage are more cost-effective for high-volume historical exports.
BigQuery charges for storage and queries. Visitor data volumes are typically small (a few GB per month). Streaming insert costs are minimal. Query costs depend on your usage patterns and can be managed with partitioned tables.
Yes. This is one of the most powerful use cases. Join Cursive visitor records with GA4 event data on user_pseudo_id or email to enrich every GA4 session with company and contact information.
You can use BigQuery ML to train classification models (logistic regression, boosted trees) on Cursive visitor data to predict conversion likelihood. Models train directly in SQL with no external ML infrastructure needed.
Yes. Connect Looker Studio (formerly Data Studio) to your BigQuery Cursive tables to build interactive dashboards and reports that auto-refresh with new visitor data.
Have a question that is not answered here? Check our pricing page for plan details, or book a call to speak with our team.
Other data warehouses tools that pair well with BigQuery and Cursive.
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