Apply AI-driven feedback analytics to your BigQuery data warehouse, eliminating the need for schema flattening. Clootrack's AI feedback analytics platform seamlessly integrates with BigQuery to analyze both structured and unstructured customer data, utilizing auto-clustering, emotional signal detection, and prioritization workflows.
BigQuery excels at managing massive volumes of structured data; however, when it comes to analyzing free-text feedback from surveys, reviews, or support logs, it requires a complementary intelligence layer. Integration with Clootrack AI bridges that gap by analyzing verbatim customer feedback stored in BigQuery, eliminating the need for data reshaping or complex pipelines.
Auto-cluster raw text feedback from BigQuery into customer-centric themes and sub-themes.
Detect emotional intensity like frustration, delight, or urgency in feedback tied to products, journeys, or regions.
Track evolving sentiment trends in real time across regions and segments.
Ask “What’s driving NPS drops in Q2?” and get instant, context-rich insight from feedback stored in BigQuery.
Trace any insight back to its original comment, segment, or feedback source, without data reshaping.
Bring survey responses, call transcripts, reviews, and CRM notes together and analyze them centrally in Clootrack.
Uncover root causes of complaints, failed flows, or feedback escalations buried in disjointed feedback data.
Get real-time visibility into signals that precede customer drop-off, even before quantitative metrics reflect the issue.
Automatically tag escalated messages or callback triggers with their underlying emotional and thematic drivers.
Ensure data from operational queues informs product fixes, service tweaks, and CX redesigns without requiring dashboards.