Find patterns in reviews with ChatGPT Scrape the reviews, then let your AI read them all

Feed your AI with real review data

A single Google review is an anecdote. Two hundred reviews across a dozen competitors is a dataset, and a dataset has patterns: the complaint that shows up again and again, the feature customers keep praising, the gap nobody in the market is filling. Reviews drive real buying behavior, with local consumer research showing the vast majority of people read them before choosing a business. The problem is that no human reads two hundred reviews and remembers them well enough to count.

Large language models do exactly that. The feed your AI with data approach is simple. You give the model a clean file of reviews, you ask a precise question, and it returns the structure. The only hard part used to be getting the reviews out of Google Maps in a format the model can read. That is the part the Vonsel extension removes.

Capture is a browser task. Open Google Maps, run your search, and the extension pulls each business with its rating, review count, and the full review text. Then you export. From there the reviews live in a spreadsheet or a JSON file, ready for sentiment analysis or theme clustering with the AI of your choice.

From Google Maps to a pattern report in four steps

The whole flow takes minutes, not an afternoon. Here is the practical sequence, the same one whether you are sizing up one restaurant or a hundred dentists in a city.

Reviews are messy free text, so the format matters. For a quick single upload, CSV is fine. For anything you want to script over, JSON keeps the reviews nested under each business, which is why it is the better feed for code. We cover the tradeoff in JSON vs CSV for AI analysis.

The fast read: one business, one upload

Start in ChatGPT when you want an answer in the next thirty seconds. Drag the exported CSV into the chat and paste a prompt that tells the model exactly what shape you want back. Vague prompts get vague summaries, so be specific about the counting.

A prompt that works: "Here are the reviews for one business. Group every complaint into themes. For each theme give the count, the percentage of reviews it appears in, and two verbatim quotes. Then list the three things customers praise most. End with the single biggest unmet need."

ChatGPT reads the file and returns a table you can act on. The value is not the summary, it is the ranking. When "slow service" appears in 40 percent of reviews and "rude staff" in 6 percent, you know which angle to lead with. That maps directly onto finding customer pain points, which is the raw material for a cold email that actually lands.

If you have not captured reviews yet, our guide on how to scrape Google reviews walks through the extension end to end. The extension can also add reviews to businesses you captured earlier, so an old list becomes a fresh AI feed without starting over.

The scaled read: a whole market at once

A single upload stops scaling once you have fifty JSON files. This is where Claude Code earns its place. It runs in your terminal, reads the folder of exports, and loops the same analysis across every business without you re-pasting anything.

Point it at the directory and describe the job: "Read every JSON file in ./reviews. For each business, extract the top three complaints and top three praises. Then aggregate across all businesses and write market-patterns.md, ranking the complaints that appear in the most businesses citywide." Claude Code opens the files, processes them, and writes the report to disk.

The output is a market map, not a single profile. You learn which pain point is systemic across the category and which is one bad operator. That is the difference between "this dentist has long waits" and "long waits are the number one complaint for dentists in this city," and the second insight is worth far more. See analyze leads with Claude Code for the setup.

Codex and other coding agents follow the same pattern if that is your stack, and a local model like a Llama or Mistral variant works when the review data cannot leave your machine for compliance reasons. The file format does not change. Capture once, feed whichever engine fits.

ChatGPT vs Claude Code for review patterns

Patterns are the input, action is the point

A markdown report is useful, but it is still a document you have to work from. The reason to run this at all is to sell something: a website, an SEO retainer, a reputation fix, a product that solves the exact complaint the reviews keep naming. The last mile is turning the pattern into contact.

That is what the Vonsel dashboard is for. The extension feeds it, and the dashboard holds every business in a mapped CRM with its reviews already analyzed. Reviews Intelligence surfaces the pain points, and Email Intelligence drafts a message per business that references the specific issue its customers complain about. You go from raw reviews to a personalized email without leaving the tool.

That is the difference between a scraper and a system. Anyone can dump reviews into a CSV. The value is a contextualized database where each lead carries the reason to call it. ChatGPT and Claude Code are how you explore the patterns. The dashboard is how you turn them into revenue.