Scrape coffee shops from Google Maps into a ready-to-sell list

A dense, local, reachable market

Coffee shops are one of the best niches to prospect on Google Maps. They cluster tightly in every city, they almost always keep a public profile with a phone and hours, and there are a lot of them: the National Coffee Association tracks daily coffee drinking at multi-decade highs, and independent cafes keep opening to meet it.

If you sell to cafes, whether you are a roaster, a POS or loyalty vendor, an equipment supplier, a delivery platform, a bakery wholesaler, or a web designer, the hard part is not finding them. It is turning that map into a clean list you can actually work. Scraping is just the fastest way to build that list.

The manual alternative is copying each cafe by hand into a spreadsheet. For a single neighborhood that is an afternoon lost. For a whole city it is not realistic. A browser extension does the same capture in the time it takes to scroll the results.

How to scrape coffee shops step by step

The whole flow runs inside your browser on the real Google Maps interface, so you see exactly what you capture. Here is the sequence:

1. Install the extension. The Vonsel extension is a free download loaded into Chrome. There is no trial timer and no card. Our full Google Maps scraping guide walks through the setup once.

2. Search like a customer. Open Google Maps and type a query such as coffee shops in Portland, specialty coffee near me, or espresso bar plus a district name. The tighter the query, the cleaner the list. Google caps a single search at around 120 results, which is why splitting by area matters (more on that below).

3. Capture the results. Open the Vonsel pop-up and start the capture. It reads each cafe on the page and pulls the fields Google exposes: business name, full address, phone number, website, primary category, star rating, and total review count.

4. Send it to the dashboard. From here you can export a CSV, XLSX, or JSON, or push the list straight into the Vonsel dashboard where the real work starts. That handoff is the difference between a spreadsheet and a value-added database.

The fields behind every cafe

Raw Google Maps rows are useful, but they are only the skeleton. Vonsel enriches each captured coffee shop so the list is ready for outreach rather than needing another round of research.

The website enrichment step visits each cafe site to look for a contact email, social handles, and a WhatsApp number. Shops with no site at all get flagged, which is a strong signal if you sell web design or online ordering.

What coffee shops actually struggle with

A list is only useful if you know what the niche cares about. Cafes run on thin margins, seasonal swings, and foot traffic, and their reviews say the rest out loud. Read a handful and the same friction points repeat.

An AI email for every coffee shop

Here is where a scraped cafe list stops being a spreadsheet and starts being a pipeline. Inside the dashboard, every coffee shop lands on a mapped CRM, and Vonsel drafts a personalized email per business using its category, rating, and review themes.

So a slow-service cafe gets an opener about your queue-busting POS, while a highly rated shop with a tired website gets a note about a fresh site. That is not a mail merge with a name swapped in. It is a different angle per shop, drawn from what the reviews actually said.

Because everything sits on a map, you can also draw a district, work only the cafes inside it, and plan a walking route for the ones worth an in-person visit. The full feature set covers capture, enrichment, review intelligence, email, and routing in one place.

Cover a whole city without duplicates

Because Google returns roughly 120 results per search, a big city needs several passes. Search by neighborhood, by nearby suburbs, or by adjacent zip codes, and capture each pass separately. The Google Maps Platform documentation explains how place results are structured and why that cap exists.

Vonsel keeps your captures in a history, so you can reopen a previous coffee shop scrape, add the next district to it, and merge without creating duplicate rows. That is far cleaner than juggling ten separate spreadsheets and deduping by hand later.

If you want to build the extension setup once and understand loading it into your browser, the official Chrome extensions documentation is the canonical reference. And before you launch a campaign, it is worth reading whether scraping Google Maps is legal for your use case.