Analyze 100+ Google reviews in less than 31 minutes 💬 🤖

EDITOR’S NOTE

Dear Future-Proof Humans,

If you’ve ever managed a product, service, or business, you know how critical customer reviews are.

Scrolling through endless feedback—from app stores, SaaS platforms, or local listings—can feel chaotic. Positive comments blend into the noise, urgent issues slip past, and crafting thoughtful replies becomes a time sink.

Sound familiar?

I’ve spent hours sifting through reviews, only to realize later that I missed recurring complaints or failed to acknowledge loyal supporters.

Maybe you’ve been there, too: juggling timely responses with meaningful analysis while keeping up with day-to-day operations.

Manually tracking trends across platforms is tedious, and copy-paste replies like “Thank you for your feedback!” rarely resonate when customers want to feel heard.

That’s why I built an automated system using Apify and ChatGPT.

It pulls reviews from any platform (Google Maps, app stores, G2, etc.), detects sentiment, groups common themes, and generates personalized replies for each user—all without manual effort.

No coding needed! 👩‍💻 Just a few tools and a straightforward setup.

In today’s edition of Nanobits, I’ll show you how to create your own review analysis assistant. Save time, spot trends instantly, and turn feedback into actionable insights.

Let’s get started.

ARE YOU NEW TO MAKE.COM?

It’s a super easy and intuitive tool to learn.

Here’s a beginner-friendly guide/primer I wrote a few weeks ago. It will help you get started with Make.com.

AI-POWERED REVIEW / FEEDBACK ANALYZER
Auto-scrape Reviews from Google Maps, G2, or Playstore, Detect Sentiment, & Generate Custom Replies 

This automation scrapes reviews from platforms like Google Maps, app stores [Google PlayStore, Apple AppStore], or SaaS tools [G2, Capterra, etc.], identifies customer sentiment, groups common themes, and crafts personalized responses—all without manual analysis or copy-paste replies.

Let’s break it down.

Click on scenarios on the left-hand side.

Click “Create a new scenario” on the top right corner of the page.

This now drops us into the scenario editor or designer, where we can build our automation.

Step 1: Set Up Apify to Scrape Reviews

Apify acts as your data collector. You can configure it to pull reviews from any platform (e.g., Google Maps, Play Store, G2). There is no coding—just input the business URL or search criteria.

• In Apify, select the “Google Maps Reviews Scraper” (or equivalent for other platforms).

• Define filters like date range or rating thresholds.

• Run the scraper. It’ll collect review text, ratings, and reviewer names.

Why This Works

• Flexibility: Works for apps, local businesses, SaaS tools, etc.

• Speed: Fetches hundreds of reviews in minutes.

Part 1

Part 2

Step 2: Connect Apify to Make.com

Make.com organizes your workflow. It takes Apify’s data and preps it for ChatGPT.

• In Make.com, create a new scenario.

• Add the Apify “Run an Actor” module. Link your account and select the scraper task.

• Test the module to ensure it pulls review data correctly.

Part 1

Part 2

The “Run An Actor“ module will only run the API. To get the data, we will add another module, “Get Dataset Items.“

For the sake of this newsletter, we will get only 10 relevant Google reviews.

I set the limit to 1 for demo purposes.

Please note that Apify offers only 3 days of free trial. Post the trial period one has to pay USD 29.99 per month.  

Let’s test the flow to see if Apify fetches the correct reviews.

The results look perfect. Now, I will increase the data limit from the scraper to 10.

Step 3: Export Review Data to Excel

Store scraped reviews in Excel for easy analysis or future use.

• Add a Google Sheets module in Make.com.

• Select “Create/Update a Spreadsheet” and link your Google account.

• Map fields like Review Text, Rating, and Reviewer Name to columns.

• Run the module. All data now populates a clean Excel sheet automatically.

As you can see below, the relevant reviews are stored in the Excel sheet under the first tab, “Reviews Analysis.“ 

Step 4: Add ChatGPT to Analyze & Personalize

ChatGPT analyzes reviews, identifies sentiments, and categorizes them into different topics. Then, write a customized response to the review on behalf of the owner/proprietor.

• Add a ChatGPT module in Make.com.

• Use a prompt like the following:

You are a restaurant who has to research the user experience of your restaurant.

You must analyze the following review and extract the main topics about the app (maximum 3 topics, minimum – 0 topics). Your response can not be N/A.

Each topic has to describe an exact feature or issue of the restaurant. It should be actionable for the restaurant owner.   

Step 1: Extract the first main topic about the restaurant.

Step 2: Extract the second main topic about the restaurant. Compare this topic with the first one. If they are similar, then only use the first topic.

Step 3: Extract the third main topic about the restaurant. Compare this topic with the first and the second ones. If they are similar, then use only previous topics.

Step 4: Check if extracted topics are really about exact features or issues of the restaurant.  If not, then return "No topics".

Step 5: If you extracted topics, then for each extracted topic, define the sentiment in the review  – positive/negative/neutral. 

If you feel that the review text has a little bit of negative but is overall positive, you may choose to return the sentiment value as positive or neutral. If you are confused, then along with the text check for ratings as well. 

Sentiment has to be related to user’s perception in the present, words such "now" could describe current sentiment. 

Here is a text of a review.
===

{{2.text}}

===

Here is the rating out of 5:
===

{{2.stars}}/5

===

Here is the reviewer name: 
===

{{2.name}}

===

Solve this task step by step.

Please respond in the following JSON format: 
{
"explanation": "explain your decision here";
"topic_name": "extracted_name";
"sentiment": "extracted_sentiment";
"response_to_review": string.
}

If the name of the person is "anonymous", then start the response with "Dear Madam/Sir". 

• Pull reviews from your Excel sheet (e.g., Review Text, Rating, and Reviewer Name) to customize each response message.

To connect Make and ChatGPT, you need an OpenAI API key, which you can find here.

Part 1

Part 2

Step 5: Export the sentiment, category of reviews, and response messages to the Excel Sheet

Export the sentiment, category, and responses directly to new columns in your existing spreadsheet.

• Update a row module (update action) to the Make.com workflow.

• Select your original Excel file and sheet.

• Map ChatGPT’s sentiment, category, and custom response to new columns D to G.

• Ensure each reply aligns with its original review.

Now, let’s examine our progress so far.

Here’s the result of one workflow iteration.

The workflow is now ready!

And just like that, we've saved some precious time by automating repetitive tasks.

End Note

Managing reviews doesn’t have to mean hours of manual work or stale replies. With this automation, you can handle feedback from apps, stores, or local businesses—while keeping responses genuine and specific.

Once it’s live, the system runs on autopilot. You’ll spend minutes reviewing pre-written replies instead of hours analyzing comments or typing answers. Setting it up takes about 30 minutes, but the time saved each week adds up fast.

I’d love to hear how you use this! Are you replying to customer complaints, tracking product feedback, or improving service quality? Hit “reply” and let me know.

Stuck? Need help setting up Apify or tweaking ChatGPT prompts? Reply to this email—I’ll walk you through it.

Until next time, work smarter—not harder.

One Last Thing: Check your filters or prompts every few weeks. Adjust rating thresholds, keywords, or reply styles to keep things fresh.