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Classify survey comments for churn risk with Claude Haiku, Supabase and n8n and get a weekly theme digest

A step-by-step build that tags each open-text survey response with themes, sentiment and a churn signal using a cheap Claude model, alerts on risky ones, and emails a weekly summary of themes.

DocumentedMeasurementSmall SaaSConnected toolsScheduled automation

Evidence: The source shows its work: steps, screenshots, code or data you can inspect.

The business problem

A SaaS founder collects survey comments but reads only a fraction of them, so churn warnings in the text go unnoticed.

What was tried

A survey with a 0 to 10 score and one comment field sends responses by webhook with metadata added from the product database. Two Supabase tables hold responses and weekly snapshots. One workflow calls Claude Haiku per response to assign one to three themes from nine, set sentiment, a churn signal and a summary as JSON, and emails an alert if the signal is raised. A Monday workflow aggregates the week, asks Claude for a short narrative and emails a digest with themes, quotes, churn patterns and one action. After about 50 responses the founder reviews ten against the tags and adjusts the definitions.

What was reported (not reported)

The author says the build takes about two hours and about $5 a month in API calls, and claims about 15 minutes of automation instead of 8 hours of manual review per cycle. No accuracy or case-study results are provided.

Limitations

The churn rules are heuristics with no evidence that they predict churn, and no classification accuracy is measured beyond a manual check of ten responses. Retry handling, the exact model version and personal-data handling for stored emails and comments are not covered, and the $5 estimate excludes n8n, Supabase, Resend and survey tool costs. The article links to the author's paid build service.

What you need

A webhook-capable survey tool, access to user metadata, and accounts for Supabase, n8n, the Anthropic API and Resend, plus basic SQL and n8n familiarity.

Sources

Source published: October 2, 2026. Last reviewed here: October 11, 2026. Spot a mistake? Tell us.

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