Most local market sizing goes wrong in one of two predictable ways: either a national figure gets divided down by population share, which ignores everything specific about the local market, or review counts get treated as a stand-in for revenue, which conflates how many people leave a review with how many people actually buy. Neither produces a number worth acting on.
Sizing a local market well isn't about landing on one precise figure; it's about replacing a guess with a defensible range, built from visible, checkable assumptions rather than a single unexplained number. Public Google Maps business data is a strong starting point for that range, but only for half of it: it shows local supply who's already competing, and roughly how strong they are, not demand. Getting to an actual market size means combining that supply-side data with a few demand-side inputs.
This guide walks through how to size a local market using Google Maps business data: what the data can and can't tell you on its own, the specific steps to build a defensible estimate, and how to pull the underlying data at scale with a Google Maps Scraper.
It's written for anyone evaluating whether to open a new location, expand a service area, or compare candidate territories before committing budget situations where "we think this market looks promising" needs to become a number that can actually be defended in a planning conversation.
What Google Maps Data Shows and What It Doesn't
A Google Maps Scraper search across a category and location returns the local competitor set: how many businesses are already operating, their ratings, review counts, and whether they have a website a structured view of local supply. That's genuinely useful business intelligence on its own, since it tells you who you'd be competing against and roughly how they're performing relative to each other.
What it doesn't show is demand: how many potential customers exist in the area, how much they spend on this category, and what share of them are likely to become customers rather than going without or driving further for a competitor. That side of the estimate has to come from a different kind of input: population and income data, plus an assumption about category penetration what portion of the local population actually buys this kind of product or service in a given year.
Step 1: Define the Trade Area
Before pulling any data, decide the geographic boundary the estimate applies to a city, a radius around a specific address, or a drive-time boundary if customers are unlikely to travel far for the category in question. This decision shapes everything downstream: too broad a trade area dilutes the estimate with population that would never realistically become customers; too narrow a one misses real demand at the edges.
Step 2: Pull the Competitor Set
With the trade area defined, a Google Maps Scraper search by category and location produces the local business directory for that market: every competing business, their address, rating, review count, and website status. Counting active listings this way gives a structured, current view of the competitive landscape closer to an actual count than an estimate based on outdated directory data.
This step also starts answering a practical question worth asking before going further: is the market saturated, or does the competitor count look thin relative to the trade area's size? A thin competitor set relative to population is often the first sign of an underserved market worth sizing more carefully.
As a quick illustration: two trade areas with similar populations might return very different competitor counts for the same category, one showing eight established businesses, another showing two. That gap alone doesn't confirm which market is the better opportunity, but it's the first signal worth investigating further, well before any revenue math happens.
Step 3: Use Reviews for Relative Strength, Not Revenue
Review count and rating are useful for one specific purpose in this process: comparing competitors' relative strength against each other, not estimating their actual revenue. A business with 400 reviews isn't necessarily generating four times the revenue of one with 100 reviews; review-leaving behavior varies by category, by how long a business has operated, and by how actively it prompts customers to leave feedback.
What review data does support reliably is ranking: which competitors appear to be capturing the most customer volume and satisfaction relative to the others in the same trade area. That relative ranking feeds into the later step of weighting obtainable market share - a market with one dominant, highly-reviewed competitor plays out very differently than one with several evenly-matched players, even if the total competitor count is the same.
Step 4: Add the Demand-Side Inputs
With the supply side built from Google Maps data, the demand side needs a few additional inputs:
● - Population: for the trade area, generally available from census or public government data sources.
● - Income for the same area, since spending power varies significantly by income and affects both category penetration and average spend.
● - Category penetration: what share of the population actually buys this category of product or service in a given year. Not everyone in a trade area buys boutique fitness memberships, dental implants, accounting services, med spa treatments, HVAC services, or legal services annually, and guessing this number wrong skews the entire estimate more than any other input.
● - Average spend: per customer per year for the category, which converts a customer count into a dollar figure.
Population and income are usually available from free public sources. Category penetration is the input most likely to need a paid industry source, an internal benchmark from existing business data, or a clearly labeled, conservative estimate, and it should be flagged as an assumption rather than presented as a hard number, since it's the piece most likely to be wrong.
Step 5: Calculate SAM and Weight SOM
Multiplying population by category penetration and average spend produces a Serviceable Addressable Market (SAM) figure, the total realistic opportunity in the trade area for this category, before accounting for competition. From there, a Serviceable Obtainable Market (SOM) estimate of the share a new or growing business could realistically capture gets weighted down based on the competitor strength assessed in Step 3. A trade area with one dominant, highly-rated competitor supports a smaller realistic SOM than one with several fragmented, weaker players, even at the same SAM.
Step 6: Compare Markets Before Deciding
A single market estimate is useful, but the real value of this process shows up when the same method is run across several candidate trade areas side by side. Running the same Google Maps Scraper pull and demand calculation across three or four candidate territories turns one uncertain number into a comparative ranking: which markets look underserved, which look saturated, and which are worth a closer second look before committing resources.
Key Features for Building the Supply-Side Data
Livescraper's Google Maps Data Scraper covers the competitor research half of this process directly:
● Category-and-location search: search by category and a specific city, zip code, or radius to pull every competing business in the trade area, including rating, review count, address, and website status.
● Multi-city support: paste a list of candidate trade areas into a single search to compare multiple markets in one pass rather than running separate searches manually.
● Filters: narrow by rating, business status, or website presence to separate genuinely active, competitive businesses from closed or inactive listings that would otherwise inflate the competitor count.
● Structured export: download the competitor dataset as CSV, XLSX, or JSON, ready to combine with population, income, and spend data in a spreadsheet.
Building the Estimate: A Practical Sequence
1. Define the trade area: city, radius, or drive-time boundary for the market being sized.
2. Pull the competitor set: using the Google Maps Data Scraper by category and location to get the current competitor list, ratings, and review counts.
3. Rank the competitors by rating and review volume to assess relative market strength, not literal revenue.
4. Gather population and income for the trade area from public sources, and settle on a category penetration and average spend assumption, clearly labeled as an estimate.
5. Calculate SAM: (population × penetration × average spend), then weight it down to a SOM estimate based on competitor strength.
6. Repeat: the same process across additional candidate trade areas to compare rather than evaluating one market in isolation.
What This Estimate Is Good For and What It Isn't
A local market size estimate built this way isn't a precise, audited figure; it's a defensible range built from visible, checkable assumptions, which is a meaningfully different and more useful thing. Its purpose is decision support: helping decide whether to enter a market, avoid it, compare it against another territory, or hold off and collect better data before committing. Treating the output as a range to sanity-check against experience, rather than a single number to trust blindly, is what keeps the whole exercise honest.
Conclusion
Public Google Maps business data answers the supply half of local market sizing well: who's competing, how many of them there are, and roughly how strong each one is. Getting to an actual market size means pairing that with population, income, and a clearly labeled category penetration assumption on the demand side, then working through SAM and SOM rather than skipping straight from competitor count to a revenue guess. A Google Maps Scraper makes the supply-side research fast and repeatable across as many candidate markets as needed for a real comparison.
Frequently asked questions
Can Google Maps data alone size a local market?
Not on its own. It reliably shows the competitor set and their relative strength, but market sizing also needs demand-side inputs like population, income, and category penetration that Google Maps doesn't provide.
Is review count a reliable proxy for competitor revenue?
Not directly; review-leaving behavior varies by category and by how a business prompts for reviews. Review count and rating are more reliable for ranking competitors' relative strength against each other than for estimating actual revenue.
Where do population and income data come from?
These are generally available from public government or census data sources for a given trade area. Category penetration, by contrast, usually needs a paid industry source, an internal benchmark, or a conservative, clearly labeled estimate.
What's the difference between SAM and SOM?
SAM (Serviceable Addressable Market) is the total realistic opportunity in a trade area before accounting for competition. SOM (Serviceable Obtainable Market) is the smaller, realistic share a specific business could capture, weighted down based on how strong existing competitors are.
What export formats does Livescraper support?
Results can be downloaded as CSV, XLSX, or JSON, ready to combine with demand-side data in a spreadsheet.
How often should a market sizing estimate be refreshed?
The competitor set can shift meaningfully within a year; new businesses open, others close. Re-running the Google Maps pull annually, or before any major decision that depends on the estimate, keeps the supply side current even if the demand-side inputs change more slowly.
