Extract Customer Pain Points from Support Forums with AI

This n8n workflow leverages AI to analyze customer support forum data and extract key pain points. It's designed to help businesses quickly identify common issues and feedback from their user base, enabling more informed product development and support strategies.

What it does

This workflow is a foundational template for AI-driven text analysis. While the provided JSON defines the core AI components, it currently serves as a blueprint for extracting structured information. To make it fully functional for extracting pain points from support forums, it would need additional nodes to:

1. Trigger: Initiate the workflow, likely by fetching data from a support forum (e.g., Bright Data, a web scraper, or an API integration).
2. Prepare Data: Format the raw forum posts into a suitable input for the AI agent.
3. AI Analysis:
   • Utilize an AI Agent (LangChain) to process the input text.
   • Employ an OpenAI Chat Model (like GPT-4, as suggested by the directory name) as the underlying language model for the AI Agent.
   • Use a Structured Output Parser to define the expected format for the extracted pain points (e.g., JSON with fields like "painPointDescription", "severity", "category").
   • Optionally, an Auto-fixing Output Parser can be used to improve the robustness of the output by attempting to correct malformed JSON responses from the AI.
4. Process Output: Take the structured pain points and perform further actions, such as:
   • Storing them in a database (e.g., Google Sheets, Airtable).
   • Creating tasks in a project management tool (e.g., Jira, Trello).
   • Sending notifications (e.g., Slack, Email).
5. Notify (Optional): Send a summary or alert via email (using Gmail, as indicated by a disconnected node) or another communication channel.

Prerequisites/Requirements

• n8n Instance: A running n8n instance to import and execute the workflow.
• OpenAI API Key: An API key for OpenAI to use their chat models (e.g., GPT-4). This will need to be configured as a credential in n8n for the "OpenAI Chat Model" node.
• Data Source: Access to customer support forum data. This workflow would require a preceding node to fetch this data (e.g., a Bright Data node, an HTTP Request node for a forum API, or a Webhook for incoming data).
• Gmail Account (Optional): If you intend to use the disconnected Gmail node for notifications, you'll need a Gmail account and its corresponding n8n credential.

Setup/Usage

1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
2. Configure Credentials:
   • Set up an OpenAI API Key credential in n8n and select it in the "OpenAI Chat Model" node.
   • If planning to use the Gmail node, set up a Google OAuth2 credential for Gmail.
3. Add Data Source Node: Add a trigger or data source node before the "Edit Fields" node to feed customer support forum data into the workflow. This could be:
   • A Bright Data node (if using Bright Data for scraping).
   • An HTTP Request node to pull data from a forum API.
   • A Webhook node if the data is pushed to n8n.
   • A Read from Spreadsheet node if forum data is in a file.
4. Configure "Edit Fields" (Set) Node: Use this node to transform the incoming data into a format suitable for the AI Agent. You'll likely need to extract the raw text of the forum posts.
5. Configure "AI Agent" Node:
   • Define the System Message to instruct the AI on its task (e.g., "You are an expert at identifying customer pain points from support forum discussions. Extract the core problem, its impact, and suggest a category.").
   • Connect the "OpenAI Chat Model" and "Structured Output Parser" as sub-nodes.
6. Configure "Structured Output Parser" Node:
   • Define the JSON schema for the output you expect (e.g., {"type": "array", "items": {"type": "object", "properties": {"painPoint": {"type": "string"}, "impact": {"type": "string"}, "category": {"type": "string"}}}}).
7. Add Output Nodes: Connect nodes after the "AI Agent" to store or act upon the extracted pain points (e.g., Google Sheets, Database, Slack).
8. Activate the Workflow: Once configured, activate the workflow to start processing data.
9. Manual Trigger: You can manually test the workflow by clicking "Execute workflow" on the "Manual Trigger" node after providing sample input data in the preceding nodes.