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Predict customer churn with AI analysis of HubSpot and Google Sheets data

PollupAIPollupAI
1104 views
2/3/2026
Google SheetsWebhookData LoggingShopifyE-commerce
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Who it’s for

Built for Customer Success and Account Management teams focused on proactive retention. This workflow helps you automatically identify at-risk customers – before they churn – by combining CRM, usage, and sentiment data into one actionable alert.

What it does

This end-to-end workflow continuously monitors customer health by consolidating data from HubSpot and Google Sheets. Here’s how it works:

  • Fetch deals from HubSpot.
  • Collect context — linked support tickets and feature usage from a Google Sheet.
  • Run sentiment analysis on the tickets to generate a customer health score.
  • Evaluate risk — an AI agent reviews deal age, sentiment score, and usage trends against predefined thresholds.
  • Send alerts — if churn risk is detected, it automatically sends a clear, data-driven email to the responsible team member with next-step recommendations.

How to set it up

To get started, configure your credentials and parameters in the following nodes:

  1. Credentials:
    • HubSpot: Connect your account (HubSpot: Get All Deals).
    • LLM Model: Add credentials for your preferred provider (Config: Set LLM for Agent & Chains).
    • Google Sheets: Connect your account (Tool: Get Feature Usage from Sheets).
    • Email: Set up your SMTP credentials (Email: Send Churn Alert).
  2. Tool URLs:
    • In Tool: Calculate Sentiment Score, enter the Webhook URL from the Trigger: Receive Tickets for Scoring node within this same workflow.
    • In Tool: Get HubSpot Data, enter the Endpoint URL for your MCP HubSpot data workflow. (Note: This tool does call an external workflow).
  3. Google Sheet:
    • In Tool: Get Feature Usage from Sheets, enter the Document ID for your own Google Sheet.
  4. Email Details:
    • In Email: Send Churn Alert, change the From and To email addresses.

Requirements

  • HubSpot account with Deals API access
  • LLM provider account (e.g. OpenAI)
  • Google Sheets tracking customer feature usage
  • n8n with LangChain community nodes enabled
  • A separate n8n workflow set up to act as an MCP endpoint for fetching HubSpot data (called by Tool: Get HubSpot Data).

How to customize it

Tailor this workflow to match your business logic:

  • Scoring logic: Adjust the JavaScript in the Code: Convert Sentiment to Score node to redefine how customer scores are calculated.
  • Alert thresholds: Update the prompt in the AI Chain: Analyze for Churn Risk node to fine-tune when alerts trigger (e.g. deal age, score cutoff, or usage drop).
  • Data sources: Swap HubSpot or Google Sheets for your CRM or database of choice — like Salesforce or Airtable.

✅ Outcome: A proactive customer health monitoring system that surfaces risks before it’s too late — keeping your team focused on prevention, not firefighting.

n8n Workflow: AI-Powered Customer Churn Prediction and Analysis

This n8n workflow leverages AI to analyze customer data and predict churn, providing insights and enabling proactive communication. It's designed to be triggered manually or via a webhook, allowing for flexible integration into existing systems.

What it does

This workflow orchestrates several key steps to perform AI-driven analysis:

  1. Triggers Manually or via Webhook: The workflow can be initiated by a manual execution within n8n or by an incoming webhook, making it adaptable to various data input methods.
  2. Initial Data Processing (Code): A "Code" node is used to prepare the incoming data for AI analysis. This likely involves formatting, extracting relevant fields, or performing initial transformations.
  3. AI Agent for Analysis: An "AI Agent" node (LangChain Agent) is employed to perform complex analysis on the prepared data. This agent is configured with an "OpenAI Chat Model" for language understanding and generation, and a "Structured Output Parser" to ensure the AI's response is in a usable, structured format (e.g., JSON).
  4. Sentiment Analysis: A "Sentiment Analysis" node (LangChain Chain) is used to determine the emotional tone of customer feedback or interactions, providing an additional layer of insight into customer satisfaction.
  5. Data Transformation (Edit Fields & Split Out): The "Edit Fields (Set)" and "Split Out" nodes are used to refine and structure the AI's output, making it ready for further processing or integration.
  6. Loop Over Items: The "Loop Over Items (Split in Batches)" node processes the AI's output in batches, allowing for efficient handling of multiple analytical results or customer records.
  7. Merge Data: A "Merge" node combines different streams of data, likely bringing together the original customer data with the AI's analytical results.
  8. HubSpot Integration: The workflow interacts with HubSpot, potentially to update customer records with churn predictions, sentiment scores, or other AI-generated insights.
  9. MCP Client Tool (Model Context Protocol): An "MCP Client Tool" is included, suggesting integration with a Model Context Protocol for managing and interacting with AI models.
  10. Send Email Notification: An "Send Email" node is used to send notifications, likely to relevant stakeholders with the churn predictions or analytical summaries.
  11. Markdown Formatting: A "Markdown" node formats data into a readable Markdown string, useful for creating reports or messages.
  12. Respond to Webhook: If triggered by a webhook, the workflow can send a response back to the originating system, confirming completion or providing results.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • OpenAI API Key: For the "OpenAI Chat Model" node to function.
  • HubSpot Account: With appropriate credentials configured in n8n for the "HubSpot" node.
  • SMTP Credentials: For the "Send Email" node to send emails.
  • Model Context Protocol (MCP) Client: If the "MCP Client Tool" is actively used, access to an MCP server or service.

Setup/Usage

  1. Import the workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API Key credential.
    • Configure your HubSpot credential.
    • Set up your SMTP credential for sending emails.
  3. Configure Webhook (if applicable): If you plan to trigger the workflow via a webhook, copy the webhook URL from the "Webhook" node and configure your external system to send data to it.
  4. Customize Nodes:
    • Review the "Code" node to ensure it correctly processes your input data.
    • Adjust the prompts and configurations of the "AI Agent", "OpenAI Chat Model", "Structured Output Parser", and "Sentiment Analysis" nodes to match your specific analytical needs.
    • Modify the "Edit Fields (Set)" and "Split Out" nodes to correctly handle and structure the AI's output.
    • Configure the "HubSpot" node to perform the desired actions (e.g., update contact properties, create tasks).
    • Customize the "Send Email" node with the recipient, subject, and body of the email.
    • Adjust the "Markdown" node for your desired output format.
  5. Test the workflow: Run the workflow manually or trigger it via the webhook to ensure it functions as expected.

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