Summarize competitor reviews with AI Export the reviews, then let ChatGPT and Claude Code read them for you

Reviews are structured pain, if you can read them at scale

Every competitor on Google Maps is sitting on a public list of exactly what their customers love and hate. Reviews are honest, specific, and dated. The problem is volume: a busy competitor can carry hundreds of reviews, and no salesperson is going to read them all before a pitch.

Reviews also matter more than most teams assume. According to the BrightLocal Local Consumer Review Survey, the large majority of consumers read reviews before choosing a local business. That means the review pile is not just noise: it is the buying decision, written out in advance.

The fix is not to read faster. It is to turn the reviews into a file and hand that file to an AI. The tool that gets you the file is a Google Maps scraper. The free Vonsel Chrome extension captures businesses straight from a Maps search and pulls their reviews into a clean export you can feed anywhere.

Export the reviews with the extension

Open Google Maps and search the category and city you care about, for example dentists in Austin. The Vonsel pop-up captures the businesses on the map. For competitor research you then add reviews to the captured businesses, so each record carries not just the name and rating but the actual review text and dates. There is a full walkthrough in add reviews to captured businesses, and a review-specific guide in how to scrape Google reviews.

When you export, pick the format that matches the AI tool you plan to use. The trade-offs are small but real, and the CSV, Excel and JSON for AI guide covers them in depth.

Whichever you choose, you now have a single file that holds every competitor and every review you captured. This is the moment the work turns from reading to prompting.

Summarize with ChatGPT

ChatGPT is the fastest route to a first summary. Upload the CSV, then ask it to do the reading. File uploads and data analysis are built into the product, and the basics of feeding text to the model are covered in the OpenAI text generation docs. A prompt that works well looks like this:

"Attached is a CSV of Google reviews for 12 competitor dental clinics. For each clinic, give me the three most common complaints and the three most common compliments. Then across all clinics, list the five recurring pain points ranked by how often they appear. Quote one representative review per pain point."

That single prompt replaces an afternoon. You get a per-competitor breakdown plus a market-wide view of what customers keep complaining about. Those recurring complaints are your opening lines: if three clinics get hammered for long wait times, that is the angle you lead with. For a deeper pattern-finding routine, see find patterns in reviews with ChatGPT.

You can push further in the same chat. Ask it to draft a cold email that references a specific weakness, or to score each competitor from 1 to 10 on how vulnerable they look. Because the data came from a clean export, the model is reasoning over real reviews, not guessing.

Summarize with Claude Code

ChatGPT is perfect for one competitor set. When you want to run the same analysis across dozens of cities, or write the summaries back to a new file, Claude Code is the better tool. It runs in your terminal, reads the exported JSON or CSV directly from disk, and can loop over every competitor without you re-uploading anything.

Drop the export into a folder and give Claude Code a plain-language instruction:

"Read competitors.json. For each business, summarize the reviews into a JSON object with fields: name, top_3_complaints, top_3_praises, sentiment_score, one_line_pitch_angle. Write the result to insights.json and print a table of the businesses sorted by weakest sentiment."

Claude Code writes the parsing, runs it, and hands you a structured insights.json you can reuse. Change the input file and rerun for the next market. That is the difference between a summary and a repeatable pipeline. The companion guide analyze leads with Claude Code shows the same pattern applied to full lead lists.

ChatGPT vs Claude Code for review summaries

Most people use both. ChatGPT for the quick look before a meeting, Claude Code when the same task becomes a weekly habit. A couple of teams also route the export through Codex or a local model when data has to stay on their own machine. The export is identical, so the tool is a preference, not a lock-in.

Where the dashboard takes over

A summary in a chat window is useful for an hour. The trouble is it lives apart from your outreach: you read the insight, then switch to another tool to act on it, and the context gets lost.

The Vonsel dashboard closes that gap. The same reviews you captured feed Reviews Intelligence, which surfaces pain points per business automatically. Those pain points sit inside the mapped CRM next to the lead, and Email Intelligence drafts a message per business that references the exact weakness the reviews revealed. You can read more in competitor review analysis on Google Maps and across the Vonsel features.

So the manual AI flow and the dashboard are not rivals. The extension plus ChatGPT or Claude Code is the fast, flexible way to explore. The dashboard is where the same data stops being an analysis and becomes a value-added database you sell from. More ideas live in the AI Workflows blog.

Quick gut checks before you start