A spreadsheet of reviews is a start. The real value is reading that sentiment as pain points and buying signals, and Vonsel does it for you.
To export a Google Maps list to CSV with its reviews, capture the businesses with the free Vonsel Chrome extension, add their reviews to each record, and choose CSV as the export format. The file holds each business with its star ratings, dates, and review text, and the same reviews stay attached to the lead in your CRM.
A business writes its own description. Its customers write the reviews. That is why review text is the most useful field you can pull off Google Maps: it tells you what people actually complain about and what they praise, in their own words.
Consumers agree it carries weight. In the BrightLocal Local Consumer Review Survey, the vast majority of people read reviews before choosing a local business. If reviews steer buyers, they can steer your outreach too. A CSV of reviews is a map of unmet needs.
The catch: exporting rows of review text is easy, but a spreadsheet does not tell you which lines are buying signals. Most of the value hides in that reading, so that is what we will focus on after the export.
Google does not offer a review export button, and the official Places API returns only a small sample of reviews per place. A browser extension reads what is already rendered on the page, so you work with the same reviews you can see. Here is the flow with Vonsel.
1. Capture the business. Open a listing or a search on Google Maps and click the Vonsel pop-up. The business is saved with its name, address, phone, website, and rating.
2. Add its reviews. On a captured business you can add reviews to the record, so the review text, star rating, and date travel with that lead instead of sitting in a separate file.
3. Export. Choose CSV, XLSX, or JSON. CSV drops straight into Excel or Google Sheets. JSON is the cleaner choice if you plan to feed the reviews to an AI model afterward.
If you prefer the step by step written for reviews specifically, our guide on how to scrape Google reviews walks through the same capture flow in more detail.
Open a raw review export and you get columns of stars, dates, and paragraphs. Useful, but you still have to read every line and decide what it means. Across a list of a few hundred businesses that is not realistic by hand.
A scraper hands you the spreadsheet and stops. Vonsel keeps going: it reads the sentiment, groups the recurring complaints, and surfaces the ones that translate into a pitch. That reading is what we call Reviews Intelligence.
Star ratings, dates, and review text in columns. You read each line, guess the pattern, and copy the useful bits into your notes by hand.
Sentiment scored, complaints grouped, and buying signals flagged per business, so a whole list of leads is triaged for you before you write a single email.
Every recurring complaint is a service someone could sell. Read reviews as a list of jobs the business has not solved, and a pattern in the sentiment becomes a pattern you can pitch to.
Vonsel flags common review signals automatically, and each one points to an offer. It can find the pain points across a full captured list, not one listing at a time.
| Review signal | What it reveals | Who can sell to it |
|---|---|---|
| "Nobody answered the phone" | Missed calls, lost bookings | Answering service, CRM, chat widget |
| "Couldn't book online" | No self-serve scheduling | Booking software, web developer |
| "The website was confusing" | Outdated or broken site | Web design, local SEO agency |
| "Slow to reply to emails" | Weak follow-up process | Email automation, CRM setup |
| Sudden run of 1-star reviews | A reputation problem right now | Reputation management service |
Star average alone hides all of this. A business at 4.2 stars can still have a clear thread of "no online booking" running through its reviews, and that thread is your opening line. For scoring a whole list this way, see review based lead scoring.
Once the sentiment is read, the business belongs in a workflow, not a download folder. Inside the Vonsel dashboard each captured lead sits on a mapped CRM with its reviews attached, so the pain point and the contact live in the same place.
From there the AI writes an email per business that references the actual complaint from its reviews. A generic "we do web design" becomes "I noticed a few of your reviews mention the booking form, here is how we would fix that." Reading the sentiment is what makes that possible, and it is covered in personalizing cold email with review pain points.
Because Vonsel lets you reopen a scrape and add more later, a list is never frozen. You can enrich businesses with their websites, add more reviews as they appear, and keep the whole thing in a saved history. The CSV is just one snapshot you can export whenever you need it.
This is also why a captured, contextualized list beats a bought database. A purchased list has no reviews and no signal behind it, a point we make in Google Maps data vs bought lists.
Reviews are public and business focused, but they are still written by people, so handle the data with care and only use it for legitimate outreach. Our overview of whether it is legal to scrape Google Maps is a good primer before you build a big list.
Keep your capture pace human, respect the platform, and focus on the B2B signal rather than individual reviewers.
If you would rather see the capture flow before you install anything, watch the video tutorial. The extension is a free download, and the pricing page lists what the Vonsel dashboard adds on top of it.
