Claude AI Customer Feedback Summary: Jira + Slack + Gmail

Turn customer feedback into actionable Jira tickets—automatically

This n8n workflow collects the past week’s customer feedback from G2, NPS, Reddit, Twitter/X, App Store reviews, Intercom, and a Postgres support-tickets table—then uses Anthropic Claude to analyze sentiment and themes, create Jira issues for top feature requests, and deliver weekly reports via Slack and Gmail.

What this workflow does

• Runs every Monday at 9am and loads API endpoints, table names, and notification targets from a configuration step.
• Fetches feedback and support signals for the last 7 days from G2, NPS, Reddit, Twitter/X, App Store reviews, Intercom conversations, and recent Postgres support tickets.
• Normalizes data into a consistent structure so every feedback item can be analyzed the same way.
• Uses Anthropic Claude to extract a sentiment score, themes, feature/bug flags, and urgency for each item.
• Saves structured analysis to Postgres, then aggregates results to calculate theme frequency and week-over-week sentiment trends.
• Detects critical alerts and posts an immediate Slack alert when critical items are present.
• Creates Jira tickets for the top emerging feature requests using Claude-generated ticket payloads.
• Sends a weekly intelligence report to a Slack channel and emails an executive-summary HTML report via Gmail.

Use cases

• SaaS teams that want faster prioritization by turning multi-channel customer feedback into Jira issues.
• Automation engineers and n8n users who need a weekly customer insights pipeline across support, product, and marketing channels.
• Product operators tracking theme frequency and sentiment trends week over week.

Technical details

• Integrations: G2, NPS, Reddit, Twitter/X, App Store, Intercom, Postgres, Slack, Jira, Gmail.
• Core logic nodes: if, set, code, merge (plus Jira/Gmail/Slack actions as described above).
• AI processing: Anthropic Claude for sentiment, themes, urgency, and ticket payload synthesis.