How to Analyze Customer Feedback with Make.com and AI Automation

Using make.com to analyze customer feedback lets you turn raw comments, survey answers, and reviews into actionable insights with AI and automation, without needing to code.

This step-by-step guide shows you how to build an automated feedback analysis workflow that classifies messages, detects sentiment, and routes insights to the right place.

Why Automate Customer Feedback Analysis with Make.com

Manual review of feedback is slow and inconsistent. With make.com and AI models, you can create a repeatable system that processes every message in the same structured way.

Key benefits include:

• Automatic categorization of issues, requests, and praise.
• Consistent sentiment scoring for each customer message.
• Centralized storage of structured feedback data.
• Instant alerts for high-priority or negative feedback.

The original make.com tutorial on AI feedback analysis provides the blueprint this article is based on.

Plan Your Make.com Feedback Automation

Before building anything in make.com, define what you want your automation to achieve and which tools will be involved.

Clarify Your Feedback Sources

List every channel where you receive customer feedback. Examples:

• Support forms and helpdesk tickets
• Email inboxes
• Website or in-app feedback widgets
• Review platforms and social media

Each source will be a possible trigger in make.com, feeding messages into your AI analysis pipeline.

Decide on AI Outputs

Next, decide what you want your AI step to return for each feedback item. Common outputs include:

• Sentiment: positive, neutral, or negative.
• Category: bug, feature request, usability, pricing, etc.
• Priority: low, medium, or high urgency.
• Summary: a short explanation of the feedback.

These outputs become data fields that you store and use for further automation in make.com.

Map Destinations and Stakeholders

Finally, choose where analyzed feedback should go:

• Spreadsheets or databases for reporting.
• Project management tools for task creation.
• Communication tools (e.g., Slack) for alerts.
• CRM systems to update customer records.

Knowing these endpoints helps you design the structure of your scenario in make.com.

Set Up Your Data Source in Make.com

Your first operational step in make.com is connecting the app or service that collects customer messages.

Create a New Scenario in Make.com

1. Log in to your make.com account.
2. Click to create a new scenario.
3. Choose your trigger module (for example, a form tool, email, or helpdesk app).
4. Authorize the connection to your external service if requested.

Once connected, configure the trigger so that every new or updated feedback item launches the scenario.

Normalize Incoming Feedback Data

Different tools send different fields. Use built-in modules in make.com to normalize this data:

• Map the customer name and contact fields.
• Combine subject and body fields if needed.
• Clean up extra formatting or system text.

The goal is to produce a single, clear text field that represents the core feedback message you will send to the AI step.

Connect AI to Your Make.com Scenario

With your trigger ready, the next step is to configure an AI module in make.com that can interpret customer messages.

Design Your AI Prompt

A well-structured prompt is crucial. In your AI module within make.com, include clear instructions such as:

• Explain that the input is customer feedback.
• Ask the AI to identify sentiment and category.
• Ask for a short summary of the feedback.
• Request a JSON or structured output format so fields are easy to parse.

Reference the text field from your trigger so the AI receives the full feedback content for each run.

Test and Refine AI Outputs

1. Run the scenario in manual mode in make.com.
2. Send in a few sample feedback messages.
3. Review the AI output for clarity, consistency, and accuracy.
4. Tweak the prompt to adjust tone, categories, or output structure as needed.

Iterate until your AI output provides reliable fields that can power downstream automation in your scenario.

Store and Organize Feedback Data via Make.com

Once the AI module returns structured data, you need to store it in a central, queryable place so it can be reused and reported on later.

Choose a Storage Destination

Typical options that connect easily to make.com include:

• Online spreadsheets (e.g., Google Sheets).
• Databases (e.g., Airtable or other cloud databases).
• Data warehouses or analytics tools.

For each feedback message, create a new record that includes raw text, sentiment, category, summary, and priority.

Enable Filtering and Reporting

Make sure the fields you store allow you to:

• Filter by sentiment to find negative feedback quickly.
• Group by category to detect frequent product issues.
• Sort by priority to drive support or product decisions.
• Track timestamps and channels for trend analysis.

With this structure in place, you can connect dashboards or BI tools to your data source and run regular reports, all powered by your make.com automation.

Act on Insights Using Make.com Workflows

The true value of automating feedback analysis with make.com comes from turning insights into concrete actions.

Route Critical Feedback to Teams

Use filters and routers in make.com to handle high-priority or negative feedback differently, for example:

• Send alerts to a dedicated Slack or chat channel.
• Create tickets in your support tool for urgent issues.
• Open tasks in your project manager for recurring problems.

Because the AI step has already tagged each message, routing rules become simple and transparent.

Trigger Follow-Up Actions

Based on sentiment and category fields, make.com can trigger additional steps such as:

• Sending thank-you emails for positive feedback.
• Requesting more details for complex issues.
• Escalating unresolved problems to managers.

This closes the feedback loop and improves the customer experience without requiring manual effort on every message.

Optimize and Maintain Your Make.com Scenario

After your automation is live, regularly review its performance and adapt it as your products and customers evolve.

Monitor Scenario Runs and Errors

Inside make.com, check execution history and error logs to identify:

• Failed connections or API changes in integrated tools.
• Unexpected AI responses or parsing issues.
• Bottlenecks caused by volume spikes.

Continuous monitoring ensures your feedback pipeline stays reliable and accurate.

Iterate on AI Prompts and Categories

Over time, you may notice new types of feedback or edge cases. Adjust your AI prompt and categories in make.com so the model recognizes:

• New product areas or features.
• Additional sentiment nuances.
• Refined priority rules for escalations.

This ongoing fine-tuning helps maintain alignment between your automation and real-world customer language.

Next Steps: Scale Your Make.com Feedback System

Once you successfully analyze one main feedback channel with make.com, you can scale the same pattern across additional sources and teams.

• Clone your scenario to handle new forms or inboxes.
• Add more destinations like CRM or marketing tools.
• Integrate dashboards for leadership reporting.

If you need expert support to architect advanced make.com workflows or integrate them with your data stack, consider partnering with automation specialists like Consultevo.

By combining structured data, AI models, and flexible scenarios in make.com, you build a scalable system that continually turns customer feedback into clear, actionable insights for your entire organization.

Need Help With Make.com?

If you want expert help building, automating, or scaling your Make scenarios, work with ConsultEvo — certified workflow and automation specialists.

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