Lead Finder by WappBlaster

Google Maps leads for restaurants

How to build a complete, de-duplicated list of restaurants for any city: the search terms that surface the category, what a metro actually yields, and the outreach approach that fits how this niche buys.

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The numbers for this niche

  • Search terms that surface the category: "restaurant", "dine in restaurant" plus your target city.
  • Typical de-duplicated list size: 5,000-40,000 in large cities.
  • Email discovery expectation: Medium. 25-40%, though many published addresses route to a franchise head office rather than the site.
  • Best first channel: Phone between service peaks (roughly 15:00-17:00 locally). Owners are on the floor at meal times and unreachable.

A single Google Maps search returns roughly 120 results however many restaurants actually exist, because the ceiling applies per query, not per city. The counts above are only reachable with grid extraction: subdivide the metro into cells, search each one, merge, and de-duplicate on place ID.

Step by step: extracting restaurants

  1. Open Lead Finder and enter restaurant plus your target city, for example "restaurant, Manchester".
  2. Enable the grid sweep so coverage extends past the 120-result cap. Dense metros justify a finer grid.
  3. Let the sweep run; progress auto-saves per cell, so a dropped connection resumes rather than restarts.
  4. Re-run with the alternative terms ("dine in restaurant") since Google's category matching is inconsistent, then de-duplicate the merged export.
  5. Export to CSV with every field, and clean before outreach: closed listings out, phones to E.164, then segment.

What to know about restaurants data specifically

The highest-churn category on Maps. A list decays visibly within a quarter: expect a meaningful share of closures and number changes, and filter permanently-closed flags before anyone dials. Delivery-platform pages sometimes shadow the real listing.

Segment by review count before anything else. In this category as in every other, a business with a handful of reviews and one with hundreds are different buyers with different budgets, and the review count is in the export at no extra effort. For the field-by-field reliability of everything else, see what Google Maps data is worth trusting.

Who buys these lists, and the pitch that works

The steady buyers of restaurants lists: pos vendors, delivery platform partners, menu and photography studios, local ad agencies.

The angle that converts: Online ordering and website refresh for venues whose listing links to a dead or outdated site.

Cost of building this list

With a flat licence ($20/year, unlimited rows), a full metro sweep of restaurants costs the same as a ten-row test, which changes behaviour: you sweep the whole city and refresh quarterly instead of rationing queries. Per-row cloud tools price the same sweep by volume; the comparison covers where each model wins.

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Frequently asked

How many restaurants can I extract from one city?
Typically 5,000-40,000 in large cities after de-duplication, depending on metro size. A single Google Maps search stops at roughly 120 results regardless of the real count, so full coverage requires grid extraction: the metro is divided into cells, each cell is searched separately, and results are merged and de-duplicated on place ID.
What data does the export include for restaurants?
Business name, phone number, website URL, full address, latitude and longitude, star rating, review count, category, opening hours where published, and social links where listed. Medium. 25-40%, though many published addresses route to a franchise head office rather than the site. Email is discovered from the linked website rather than the listing itself, because Google Maps has no email field.
Who buys restaurants lead lists?
The consistent buyers are pos vendors, delivery platform partners, menu and photography studios and similar businesses selling into the niche. The outreach angle that converts best: online ordering and website refresh for venues whose listing links to a dead or outdated site.