BI & Analytics
Google BigQuery integration with Feedback Analytics, analytics at scale
Feedback Analytics captures structured feedback and turns it into insight and follow-up. BigQuery is where that data meets orders, CRM, and operations, so data, BI, and leadership teams see trends by segment, region, and time in one warehouse.
Why this integration
Feedback often sits apart from revenue and operations data. Landing Feedback Analytics data in BigQuery lets you join signals with the metrics you already trust, NPS and CSAT alongside operational KPIs.
Value grows when the chain is sound, reliable collection in Feedback Analytics, then scale with SQL and BI in BigQuery.
What you can do with Feedback Analytics + Google BigQuery
Feedback Analytics stays the system where feedback is measured and interpreted; BigQuery is where you blend it with the rest of your data estate.
| Central storage | Load responses, scores, and metadata into BigQuery, ready for SQL, views, and pipelines. |
|---|---|
| Join operational data | Combine feedback with orders, CRM, product events, or locations for integrated analysis. |
| Scale analytics | Cohorts, trends, and segmentation over long horizons, without manual spreadsheet cycles. |
| Feed dashboards | Power Looker Studio, Power BI, or other tools on BigQuery with consistent definitions. |
| Technical shape | APIs, exports, and data pipelines, aligned with how your data team runs ETL and governance. |
Practical use cases
- NPS and CSAT trends by region or store, joined to operational KPIs in the warehouse.
- Themes from open text, aggregated by product line or segment for prioritization.
- Post-service feedback, linked to case or order data to connect satisfaction and throughput.
- Executive reporting, one dataset for finance, operations, and CX.
- Data science, features for models using historical feedback and behavior.
Who it is for
Data and BI teams, management, and operations that need feedback in the same numbers as the rest of the business, with governance and lineage.
How the connection works
Configure surveys and logic in Feedback Analytics; use exports or API access for downstream processing.
Route data to BigQuery via your chosen pattern, scheduled loads, pipelines, or integrations that match GCP and security policies.
Why Feedback Analytics as the central feedback layer?
Feedback Analytics covers collection through action, flows and action lists, not one-off surveys.
Data sent to BigQuery stays meaningful because you know how scores were produced and what follow-up already ran.
Want to map this to your GCP stack, access controls, and pipelines? Request a demo, we’ll walk through your first use case and quick wins for data and BI teams.
FAQ
Does BigQuery replace Feedback Analytics?
No, BigQuery is analytics storage; Feedback Analytics is where feedback is captured and turned into follow-up.
Do we need data engineers?
Simple paths may only need clear exports. Complex models and production workloads usually involve data engineering, like other warehouse sources.
What about privacy?
Send only required fields, document purposes and retention, and align with processor and cloud policies.
Can we analyze NPS and CSAT together?
Yes, model both in BigQuery and join with segment or location fields for comparisons over time.
