Ten GBP scans is not a large dataset. But for a specific industry in a specific city, ten scans of direct competitors in the same service category is enough data to write a credible, publishable local market report that positions your agency as the authority source on that market.
To learn more about building local authority with scan data, visit Run a Local Ranking Heatmap and Find Dead Zones. How to Read a Geogrid and Build a Local SEO Action Plan and Plan a Client Content Calendar With Funnel Mapping cover adjacent steps in detail.
Most agencies treat scan data as a prospecting tool. It is also a publishing asset. A market report built from real scan data, with named findings and category-specific conclusions, is shareable, linkable, and demonstrates capabilities in a way that a services page never can.
This article explains how to structure the report, what data to extract from the scans, and how F! Insights generates the first-signal market report from as few as 10 completed scans through the Market Intel tab.
In This Article
What 10 Scans Actually Give You
Data available from 10 GBP scans in the same category.
| Data Category | What 10 Scans Reveal |
|---|---|
| Review data | Average review count, average star rating, review velocity distribution across the category |
| Profile completeness | How many businesses have complete vs incomplete GBP profiles; which elements are most commonly missing |
| PageSpeed | Average mobile PageSpeed score for the category; distribution of scores |
| Competitive position | Which businesses are consistently in the Map Pack vs outside it |
| Photo activity | Average photo count per profile; how many businesses are posting photos at all |
These averages become the benchmarks in your report. “The average HVAC company in Columbus has 34 Google reviews and a mobile PageSpeed score of 41” is a specific, verifiable finding that no competitor report in this market is publishing, because no one else has done the scan.
Report Structure That Works
- Executive summary: 3 to 5 top findings in plain language. This is what gets shared.
- Market overview: Category, city, scan methodology, and date range. Establishes credibility and reproducibility.
- Finding 1: Review landscape. Average count, distribution, velocity. Who is winning on reviews and by how much.
- Finding 2: Profile completeness. What percentage of businesses in this category have complete profiles. What elements are most commonly missing.
- Finding 3: Website performance. Average mobile PageSpeed score. How many businesses score below 50.
- Finding 4: Competitive gap analysis. The gap between the Map Pack leaders and the average business in the category. What the leaders have that others do not.
- Implications for local businesses: Plain-language translation of what the data means for any business in this category.
- Methodology note: How the scans were conducted. Transparent methodology is what makes the report citable.
Extracting Findings by Category
From the F! Insights scan data export, calculate the following for the 10 businesses in your report:
- Mean, median, and range for review count and star rating
- Mean, median, and range for mobile PageSpeed score
- Percentage of profiles with complete business descriptions (keyword and city in first sentence)
- Percentage of profiles with complete service lists
- Percentage of profiles with post activity in the last 30 days
- Percentage of profiles with complete attribute sets
Present each finding as a comparison between the category average and the Map Pack leaders. The gap is the story. The gap is also your sales angle for every business in the category that falls below the benchmark. See Local SEO Benchmarks: What Good Actually Looks Like for context on what benchmarks look like across categories.
Where and How to Publish
- Publish on your agency website as a PDF landing page with an email gate or a free download. The download gives you an email list of people interested in local SEO in this specific category.
- Share in local business association groups, industry forums, and relevant LinkedIn communities for the scanned category.
- Email the report directly to the businesses in the scan as a value-first outreach. “I scanned 10 HVAC companies in Columbus. Your profile came up. Here is how you compare to the category average.”
- Use it as the foundation for a prospect hit list. The businesses that score below the category average on two or more metrics are your priority outreach targets. See How to Build a Prospect Hit List From Your Local Scan Data.
How F! Insights Generates the Market Report
F! Insights generates a first-signal market report automatically when your scan library hits 10 completed scans in the Market Intel tab. Claude analyzes the scan data across all eight audit categories, identifies the most significant patterns, and generates a formatted report with findings, benchmarks, and category-specific conclusions. The report is exportable as a formatted document ready for publication or client delivery.
As your scan library grows, F! Insights unlocks additional Market Intel tiers at 25, 50, 100, and 200 scans. Each tier generates more sophisticated outputs: pitch decks, competitive gap analyses, annual market reports, and media-ready research assets. Run a free GBP scan on any local business to start building the dataset.
Related reading: For the full workflow for publishing a local market report including distribution, see that guide. The compounding argument for publishing market research that builds authority is explained there. The report framework maps to what local SEO benchmarks look like across markets for the niche you scanned. For which niches produce the most consistent scan data, which tells you where a 10-scan report will be most compelling, see that breakdown.
Frequently Asked Questions
- Do I need permission from the scanned businesses to publish their data?
- GBP data surfaced through the Google Places API is publicly available information. Aggregated findings do not require permission from individual businesses. If you include specific named businesses in the report, their GBP data is public, but standard practice is to use aggregated data in public reports and reserve named business data for direct outreach to those businesses.
- Can I run this report for multiple cities and combine them?
- Yes. A multi-city report covering the same category across five cities in the same state is a stronger publishing asset than a single-city report and positions your agency as a regional authority rather than a local one. Each city’s data remains a separate dataset for the per-city findings, with a regional summary as the lead section.
- What should a local market intelligence report contain?
- A local market intelligence report based on GBP scan data typically contains: the average GBP health score for businesses in the niche and market, the three most common scoring gaps across all profiles scanned, the correlation between GBP score and review count in that market, and a ranking of which categories show the widest gap between top and bottom performers. Ten scans is enough data to produce all four sections with specific, citable numbers.
- Who is the intended audience for a local market intelligence report?
- Two audiences: the businesses in the niche who want to know how they compare to competitors, and media and industry publications that cover the niche or the local market. Restaurants, real estate agencies, and healthcare practices are categories with active trade publications that regularly publish local market data. A well-structured report from fifteen to twenty scans can generate earned media coverage that paid advertising cannot replicate.
- Can the scan data in a local market report be presented as original research?
- Yes, and that is exactly how it should be framed. The scan data is proprietary. It was gathered by running your own queries through the Google Places API from your own F! Insights installation. No other source has the same data. Framing the report as “scan data from [month] across [niche] in [city]” is accurate and positions it as original primary research, which carries more credibility than secondary reporting on publicly available data.