Background
A small service business received customer queries through multiple channels, email, contact forms, and chat messages. The team manually read and categorised each query by type and urgency before assigning them to the right staff member.
This process was time-consuming, error-prone, and sometimes led to delayed responses or overlooked messages, affecting customer satisfaction. The business needed a smarter system to ensure every query was handled quickly and efficiently.
Challenge
The business wanted to:
- Automatically sort incoming queries by category and urgency
- Reduce manual reading and assignment of messages
- Ensure no customer query went unanswered or delayed
- Free up staff to focus on solving issues rather than managing messages
The goal was simple: respond faster, reduce human error, and improve customer satisfaction.
Our Solution
We set up an AI-powered customer query categorisation workflow entirely on Make.com:
- Centralise Incoming Queries
- Connect all sources of customer messages (Gmail, contact forms via Google Forms, or a chat platform) to Make.com.
- Each new message automatically triggers the workflow.
- Analyse & Categorise Queries
- Use Make.com’s AI or text analysis modules to automatically read the content of each message.
- The AI determines:
- Query Type: Support, Billing, Feedback, General Inquiry
- Urgency: High, Medium, Low
- Route Queries Automatically
- Based on category and urgency, Make.com routes the message to the correct staff member or department:
- High urgency → instant Slack/Email notification to the responsible person
- Low urgency → queued in a shared task list or spreadsheet
- Based on category and urgency, Make.com routes the message to the correct staff member or department:
- Log & Track Messages
- All queries and their AI-assigned categories are logged in Google Sheets or Airtable automatically.
- This allows management to track response times and query types for reporting.
- Optional Enhancements
- Send auto-replies for confirmation (“We received your message and will respond shortly”)
- Generate weekly reports showing query volumes, categories, and average response times
Impact & Results
| Metric | Before | After |
|---|---|---|
| Time spent categorising queries | 5–10 hours/week | <30 minutes/week |
| Response delays | Common | Almost none |
| Errors in assignment | Frequent | Rare |
| Manual effort | High | Fully automated routing |
Key Outcomes
✅ Saved several hours of manual work each week
✅ Faster response times, improving customer satisfaction
✅ Reduced errors in query assignment
✅ Staff can focus on resolving issues rather than managing messages
The business now handles customer queries efficiently, ensuring timely responses, happier clients, and a more productive team.