Local Market Research With Google Maps Data

Most writing about Google Maps data assumes you want to sell to the businesses. Sometimes you want to count them instead.

How saturated is this category here. Where are the competitors clustered. Which neighbourhood is underserved. Is this market established or still forming. These are supply questions, and listing data answers them reasonably well, as long as you are honest about the several ways it can mislead you.

What listing data can and cannot tell you

Worth fixing this in place before running any numbers.

It can tell you: how many businesses in a category are visible in an area, where they sit geographically, how established each appears by review volume, how they are rated, and roughly what price band they present.

It cannot tell you: revenue, employees, floor space, ownership structure, whether two listings belong to one company, footfall, or market size in currency. None of that is in a listing, and no amount of processing puts it there.

So the honest framing is that you are taking a census of visible supply. That is genuinely useful and it is not the same thing as measuring a market.

The rating trap

The first instinct with this data is to compare average ratings between areas. It is the wrong metric, and our own numbers show why.

Across a sample of 3,147 listings spanning 20 countries and 58 cities:

• Average rating ranged from 4.38 to 4.78. The entire spread across twenty countries is four tenths of a star.
• Median review count ranged from 2 to 320. That is a 160-fold difference.
• The share of listings with zero reviews ranged from 4.1% to 45.2%.

Australia sat at a median of 320 reviews with an average rating of 4.76. Argentina sat at a median of 2 reviews with an average rating of 4.68. The ratings are nearly identical. The review behaviour is from different planets.

Ratings compress toward the top everywhere, because unhappy customers mostly leave rather than review and the scale is not used as a scale. A market where the average is 4.6 and one where it is 4.5 are not meaningfully different, and building an analysis on that gap is measuring noise.

Review volume is where the variation actually lives. Use it, and benchmark it locally, because the country-level baselines differ by more than an order of magnitude. Full country figures are in our review coverage benchmark.

Measuring density properly

Density is a comparison, never an absolute. "There are 47 dentists" means nothing. "There are 47 here and 12 there, counted the same way on the same day" is a finding.

Three rules make the comparison hold up:

1. Identical query across every area. The same category term, the same target size, the same run. Changing the search term between areas invalidates the comparison, because Maps categories are not a controlled vocabulary.
2. Normalise by something. Listings per 10,000 residents, or per square kilometre. Raw counts mostly measure how big the area is.
3. Define areas that are comparable. Cities of wildly different size are not comparable units. Drawing equivalent areas is usually better, and scoping a search to a drawn area covers how.

Every exported row carries latitude and longitude, so once you have the data, clustering is a spreadsheet or mapping exercise. Plotting the points is worth doing early. Density numbers hide the thing you usually want, which is that all fourteen competitors sit on one street and the entire east side has none.

A workable method

1. Define the candidate areas and write down what makes them comparable.
2. Run one identical export per area, same category and target size.
3. Deduplicate on name plus address. Chains and branch listings inflate counts, and one company with six locations is not six competitors.
4. Count and normalise, then plot the coordinates.
5. Segment by maturity using review count. A category where most listings sit above the local median is established. One where half have almost no reviews is forming, churning, or seasonal.
6. Check the composition, not just the total. Twenty listings that are all one franchise is a different market from twenty independents.

For screening several markets at once against a consistent set of gap measures, the local market opportunity index publishes the scoring model we use and the template to edit.

Where this data lies to you

Four biases, all of which distort counts in one direction and none of which cancel out.

Listings are not a registry. A business with no listing is invisible, and the businesses least likely to maintain one skew toward the small, the informal, and the very new. Your count is of listed supply.

Branches inflate. Multi-location companies appear once per location. Without deduplication you will read one regional chain as a competitive field.

Categories are self-assigned. Owners pick their own label and pick inconsistently. This is the same effect that makes category choice matter so much when prospecting, which we measured in finding businesses without websites: searching "dentist" and "dental clinic" surfaces overlapping but materially different sets.

Results reflect the search, not the territory. What comes back depends on the query, the target size, and how Maps ranked things that day. Two runs of the same search are similar rather than identical, so record the date and the parameters with the numbers.

The single best defence is to re-run a comparison you care about a few weeks later. Findings that survive both runs are real. Findings that move are artefacts.

Freshness

Listing data ages. Businesses close, move, and rebrand, and a snapshot taken months ago quietly stops describing the place.

For prospecting, mild staleness costs you a wasted call. For market research it is worse, because a stale count silently becomes a wrong conclusion that then gets acted on. Note the collection date next to any figure you publish internally, and re-pull before anyone spends money on the basis of it.

The business_status field is a useful sanity check here, since it flags listings that are marked closed rather than leaving them to sit in the count as though they were trading.

FAQ

Can Google Maps data be used for market research? Yes, for questions about supply. Listings tell you how many competitors operate in an area, how they cluster, how established they are by review volume, and where a category is thin. They cannot tell you revenue, headcount, or market size, so treat them as a census of visible supply rather than a measure of the market.

How do you measure competitor density from listing data? Count listings per category within a defined area, then normalise by something comparable such as population or area, and repeat the identical query across the areas you are comparing. The absolute count is nearly meaningless on its own. The comparison between areas measured the same way is the finding.

Is average rating a useful way to compare markets? Barely. Across 20 countries in our sample, average rating ranged only from 4.38 to 4.78, while median review count ranged from 2 to 320. Ratings compress toward the top everywhere, so differences between areas are mostly noise. Review volume carries the signal.

What does review count actually measure? It is a rough proxy for accumulated trade and listing age combined, not for current revenue. A business open ten years with steady custom outranks a busier business open one year. It is most useful as a relative measure inside one area and category, and least useful across countries.

What are the biggest biases in Maps-based market research? Businesses without listings are invisible, one company with several branches appears as several records, categories are chosen by owners rather than assigned, and result sets reflect what the search surfaced rather than a complete registry. Each of these inflates or deflates counts in ways that do not cancel out.

Is Maps data enough to decide where to open a location? No. It is a good screening layer for narrowing a long list of candidate areas to a short one, and a bad basis for the final decision. Rent, footfall, catchment demographics, and licensing are not in the data, and they are usually what determines the outcome.

Get started

At basedonb.com, run the same category across the areas you are comparing and export the coordinates with the rest of the row. A new account includes 50 one-time export credits with no card. Paid plans are billed on day one and carry a 7-day money-back guarantee on the value of unused credits.