Building a Customer Churn Prediction Workflow

Start Simple

Begin with behavioral triggers rather than building a comprehensive system from scratch. This prevents alert fatigue and improves team adoption.

Initial Setup: Behavioral Triggers

Use HubSpot's native workflow builder to create alerts based on:

• No login for X days
• Declining health score trends
• Missed meetings
• Support ticket spikes

Route alerts to Slack via Zapier or HubSpot's native integration.

Adding Conversation Intelligence

For transcript analysis, use dedicated tools rather than DIY solutions:

• Gong or Chorus - both offer built-in sentiment analysis and keyword detection
• Both integrate with HubSpot and can trigger workflows based on conversation insights

Email Sentiment Analysis (Optional)

Tools like MonkeyLearn or AWS Comprehend can analyze email sentiment, but only pursue this if you have significant email volume to train on.

Threshold Tuning

Start conservatively with combined signals. Example:

• Health score drops below 6/10 AND no login in 14 days

Monitor for one month, then adjust based on results. Avoid creating 50+ daily alerts that get ignored.

Include Expansion Signals

Monitor for positive indicators alongside churn risk:

• Sudden increase in feature usage
• Expansion conversations in transcripts
• Integration questions

Foundation Question

Establish your health score methodology first—this matters more than the automation layer itself.

Building a Customer Churn Prediction Workflow

CRM-Based Alerts

Use HubSpot native scheduled workflows to evaluate and alert on:

• Health scores
• Last login dates
• Last meeting dates
• Push alerts to Slack

Email Analysis

1. Pull email data from HubSpot CRM Calls API
2. Use automation tools (n8n or Make) to process the data
3. Trigger Slack alerts when specific keywords are detected

Transcript & Keyword Analysis

1. Pull transcripts from your transcript platform (e.g., Gong) via API
2. Use Make or n8n to run keyword detection analysis
3. Route results to Slack alerts

Historical Transcript Storage

For accessing historical transcripts without repeated API calls:

• Database approach: Set up a lightweight database (even Google Sheets) to store historical transcripts by domain, upserting only new records on recurring pulls
• Lovable approach: Use Lovable with its built-in cloud database to store historical transcripts and configure Slack alerts directly

The database approach adds maintenance overhead but provides greater flexibility for local search and analysis.

Incorporate Third-Party Data

Add external signals to your churn prediction model to identify at-risk customers before they take action:

• Leadership changes — New CEO or executive hires often trigger strategy shifts, cost-cutting measures, or vendor consolidation
• Acquisition activity — Private equity acquisitions frequently result in operational changes and vendor reviews
• Workforce changes — Layoffs and headcount reductions signal budget constraints and potential service cancellations

These signals typically emerge before customers formally notify you of churn intent, giving you a window to intervene proactively.

Building a Customer Churn Prediction Workflow

Tools & Platforms

You can build this workflow using automation platforms such as:

• Make
• Zapier

Key Requirements

• Integration with your CRM
• Integration with call recording systems
• Access to call transcripts

Important Limitation

Retroactive analysis is not possible with most automation workflows. The system cannot process historical call transcripts—it can only analyze new data going forward. Functionality may improve over time, so verify current capabilities with your chosen platform.

NULL

The original answer contains no actionable information—it only expresses uncertainty about whether Make can handle the task and mentions openness to other tools, without providing any actual guidance on building a customer churn prediction workflow.