Ideal customer profile template for local B2B, built from Google Maps data

What an ideal customer profile is (for local sales)

An ideal customer profile describes the type of business you close fastest and keep longest, written as concrete attributes rather than adjectives. For local B2B the useful version is firmographic and geographic: what the business does, where it sits, how established it looks, and whether it shows the gap your product fixes.

The classic mistake is to write an ICP as prose ("small, ambitious, growth-minded local shops") that no one can filter on. The map-native version lists firmographic attributes that correspond to real, public fields on a Google Maps listing. That single shift is what turns an ICP from a poster on the wall into a query you can run tomorrow morning.

The extension is the means, not the end. It reads those fields off the map so your ICP becomes a live filter, and the Vonsel dashboard is where matched accounts become a mapped CRM, contextualized databases and AI email per business.

The local ICP template, row by row

Copy this table and fill the right column for your business. Each attribute lines up with a field the extension captures, so what you write here becomes the filter you apply after a capture.

Notice the rating band is deliberately not "5.0". A business at 3.8 with a stack of complaints is often a better prospect for a reputation, web or review service than a spotless one, because the pain is visible in the reviews. That is a signal you can only use if you keep the raw fields.

Which fields carry each signal

An ICP is only as good as the data behind it. On Google Maps the attributes above come from public listing fields, the same ones documented in the Google Maps Platform Places data fields reference. Category, formatted address, geometry, user rating count, rating, website and price level are all first-class fields, which is exactly why they make dependable filters.

When you scroll a search, the extension captures those fields for every pin on screen. You can read the full list in Google Maps data fields explained. The point for your ICP: if an attribute cannot be tied to a field, it cannot be a filter, so leave it out of the template and keep the profile executable.

Place ID reverse lookup: how to enrich a match

Every listing has a stable Place ID, a permanent identifier for that exact business location. It is the anchor for a reverse lookup: give the ID, get the current record. The identifier and its behavior are described in the Google Maps Platform Place ID documentation.

Because the Place ID does not change when a business edits its name or moves within the same location, it is the safe way to dedupe and segment a list. In Vonsel you can reopen a previous capture, add more listings to it, and attach reviews to businesses you already stored, so an ICP list stays current instead of going stale in a spreadsheet.

Add location signals your competitors ignore

Firmographics tell you what a business is. Location intelligence tells you where it sits relative to demand, and that is often the tiebreaker. Coordinates let you filter by radius, cluster prospects by neighborhood, and prioritize the ones near an existing customer or a route you already drive.

Two location signals worth adding to the template once the basics are working:

The mapped CRM inside Vonsel plots every matched account, so a location-aware ICP is not theoretical: you see the clusters, draw the zones, and hand a rep a route. That is the end the extension is a means to.

Turn the template into a capture in four moves

1. Translate each ICP into a query. "Dentists in Madrid, 20 to 300 reviews, no website" becomes the search "dentist Madrid" plus a filter you apply after capture. One profile usually spawns several queries, one per city or district.

2. Capture the results. Open Google Maps, run the search, and let the extension read every pin as you scroll. Remember the platform returns about 120 results per query, so split large cities into districts rather than fighting the cap.

3. Filter before export. Apply your rating band, review count and website rule. What survives is your ICP-fit list, not the raw map. For the wider case on why targeted beats bulk, see prospecting with Google Maps data.

4. Act in the dashboard. Matched businesses land in the mapped CRM with review analysis and an AI email drafted per business, so the profile ends in outreach, not another CSV. Curious whether any of this crosses a line? Start with is it legal to scrape Google Maps.