Google Maps Lead Opportunity by Category: 2026 Dataset
Category choice often matters more than list size in Google Maps prospecting. A thousand mixed businesses may contain fewer relevant opportunities than one carefully defined segment of fifty. To make that choice more evidence-based, we scored the observable data gaps in 3,147 local business profiles covering 253 exact category labels.
The downloadable category opportunity dataset includes the 33 categories with at least ten sampled records. It reports missing website links, missing phone numbers, profiles missing both and profiles with zero reviews. Our related businesses-without-websites study explains the website-field caveats in more detail.
The highest observed scores
| Category | Sample | Website gap | Phone gap | Zero reviews | Score |
|---|---|---|---|---|---|
| Breakfast restaurant | 18 | 72.2% | 5.6% | 27.8% | 41.1 |
| Family restaurant | 35 | 65.7% | 28.6% | 11.4% | 40.6 |
| Bar & grill | 15 | 60.0% | 20.0% | 13.3% | 35.3 |
| Doctor | 13 | 53.8% | 0.0% | 23.1% | 30.0 |
| Fast food restaurant | 39 | 43.6% | 12.8% | 25.6% | 29.1 |
These are observed gaps, not a ranking of commercial attractiveness. Several leading categories have small samples, and the dataset is heavily weighted toward restaurants and dental services. A high score means “more incomplete fields under this formula,” not “easy customers” or “bad businesses.”
How the score works
The score is a weighted combination on a 0–100 scale:
- 45% website-link gap
- 15% phone gap
- 15% missing both website and phone
- 25% zero-review share
The weights reflect common prospecting use cases: a website gap can indicate a web-service conversation, contact gaps increase verification cost, and zero reviews may indicate a reputation or profile-development need. The weights are deliberately visible so that another team can disagree and calculate its own version.
For example, a call-center campaign may give phone coverage much more importance. A web agency may care primarily about owned-domain status. A reputation consultancy may emphasize review depth rather than the zero-review threshold. The raw component columns make those alternatives possible.
Sample size before score
A category with ten records can move dramatically when two more records are added. A category with 949 records is much more stable, although it can still be biased by city and country concentration. Always read the sample_size column before the score.
A practical rule is to use small categories for exploration and large categories for planning. For an exploratory segment, manually review every record. For a larger category, stratify by country or city and compare subgroups. Never present a category percentage without its denominator.
From category label to campaign hypothesis
The exact Google Maps category should start the research, not finish it. Two businesses under the same category can differ in size, customer model and digital maturity. Convert the category into a testable hypothesis:
Independent family restaurants in City X with a phone number, no visible owned website and at least 20 reviews may be suitable for a direct-order landing-page offer.
That statement is better than “restaurants need websites” because it defines geography, observable filters and a specific offer. It can be tested on a small batch and rejected if the evidence is weak.
Google asks businesses to use categories that describe what the business is, rather than every service it offers. Its official category guidance is useful when interpreting exact and secondary labels.
A four-stage qualification process
Stage 1: observable filters
Filter by category, location, website field, phone field, rating and review count. Keep the raw source URL and collection date.
Stage 2: identity and duplicate checks
Normalize names, phones and domains. Branches of the same brand may be valid separate records; spelling variants of one location may not be.
Stage 3: manual business-context review
Open the listing and linked destination. Determine whether the apparent gap is real and whether the proposed service makes sense. A social-first business can be intentional; a franchise location may have no authority to buy a website.
Stage 4: outcome measurement
Track verified-fit rate, reply rate and qualified conversations by category. If a high-scoring category produces poor conversations, the commercial hypothesis—not the data extraction—may be wrong.
What makes this dataset link-worthy
The reusable part is not the ranking alone. It is the combination of downloadable components, explicit weights and limitations. Researchers can rerun the model with different weights; agencies can compare their own batch; editors can cite a number with its denominator.
Bottom line
Category selection is a modeling decision. In this sample, observable gaps vary substantially across categories, but small denominators and concentrated sampling require restraint. Use the score to decide what to inspect next, then validate the segment with current records and real campaign outcomes.