Customer feedback automation with sentiment analysis using GPT-4.1, Jira & Slack

Customer Feedback Automation Workflow with Webhook, OpenAI, Jira & Slack

This workflow collects customer feedback from a webhook, validates the incoming data, analyzes the sentiment using OpenAI and creates Jira tasks for negative or feature-request feedback. It also generates an automated weekly summary using OpenAI and delivers it to Slack. It helps teams stay informed, skip manual reviews and act quickly on customer issues.

Quick Start – Implementation Steps

1. Set up the Webhook URL in your application to send customer feedback. 2. Configure Slack, Jira and OpenAI credentials in n8n. 3. Adjust sentiment rules or Jira fields if needed. 4. Activate the workflow — you’re ready to collect and process feedback automatically.

What It Does

This workflow automates the entire lifecycle of customer feedback handling. When someone submits feedback, the system checks if the required information (feedback text and sentiment) is present. If the payload is invalid or incomplete, the team immediately receives a Slack notification to take action.

If the feedback is valid, OpenAI analyzes the content and identifies the sentiment as positive, negative, neutral or a feature suggestion. Based on this result, the system automatically creates a Jira issue for negative feedback or feature requests, ensuring nothing important is missed.

Alongside real-time processing, the workflow also compiles a weekly summary. Every week, it gathers all Jira issues created through feedback and sends them to OpenAI for summarization. The summary is then posted to Slack so the team gets a clean, easy-to-read review of customer sentiment trends.