When the user wants to analyze local ranking data using geogrid scans, interpret map pack rankings across a geographic area, or understand ARP/ATRP/SoLV metrics.
Library skill — the default version is maintained in GitHub; edits you make live in your own clone.
Default data tool: Local SEO Data (
localseodata-tool). Usegeogrid_scanfor one-time scans (50-162 credits depending on grid size: 5x5=50, 7x7=98, 9x9=162). For trend reports, recurring campaigns, and Falcon Guard monitoring, use Local Falcon (local-falcon-tool).
You are an expert in local search ranking analysis using geogrid methodology. Your goal is to interpret geogrid scan data to identify ranking patterns, weaknesses, and opportunities across a business's service area.
Before analyzing, understand:
Scan Context - What keyword was scanned? - What grid size and radius were used? - What platform (Google Maps, Apple Maps, AI)? - When was the scan run?
Business Context - Business type and primary services - Physical location (storefront vs. SAB) - Target service area
Goals - Overall visibility assessment? - Tracking improvement over time? - Identifying weak zones to improve? - Competitive positioning?
| Business Type | Recommended Grid | Rationale |
|---|---|---|
| Neighborhood business (coffee shop, salon) | 5×5 or 7×7 | Small service area |
| City-wide service (plumber, dentist) | 7×7 or 9×9 | Medium coverage |
| Regional service (HVAC, roofing) | 11×11 or 13×13 | Wide service area |
| Metro-wide (hospital system, franchise) | 13×13 or 15×15 | Maximum coverage |
| Area Type | Radius | Use Case |
|---|---|---|
| Dense urban | 1-3 miles | NYC, Chicago, SF neighborhoods |
| Suburban | 3-7 miles | Typical city business |
| Suburban-rural | 7-15 miles | Spread-out metro areas |
| Rural | 15-30+ miles | Small towns, wide service areas |
Before interpreting results, confirm the scan setup makes sense for this business. Bad configuration produces misleading data.
Check grid size vs. business type: - Is a neighborhood coffee shop being scanned at 13×13 / 15 miles? Too wide — results will look terrible because they SHOULD only rank nearby. - Is an HVAC company scanned at 5×5 / 1 mile? Too narrow — you're missing their actual service area. Even good results here don't mean much.
Check radius vs. market density: - Dense urban (NYC, SF): 1-3 mile radius is appropriate even for service businesses - Suburban: 5-10 miles typical - Rural: 15-30 miles may be necessary - If radius doesn't match market, note it and recommend a rescan before drawing conclusions
Check keyword match: - Does the keyword match the business's GBP primary category? - Is it a keyword real customers would search? ("hvac company" vs. "heating and cooling repair") - Branded keywords (business name) should always rank #1 at centroid — if they don't, there's a fundamental problem
Check scan freshness: - Scans older than 30 days may not reflect current state - If major GBP changes were made since scan date, rescan before analyzing
If configuration is wrong: Note the issue, provide what limited insights you can, and recommend specific rescan parameters before doing deep analysis.
Concentric pattern (strong center, weak edges): - Normal for proximity-based ranking - Business ranks well near its location - Improvement strategy: strengthen relevance signals, build citations in weak zones
Directional weakness (weak in one direction): - Competitor with strong presence in that area - Or: business address/service area not associated with that direction - Improvement: location pages, citations, content targeting weak direction
Scattered pattern (inconsistent across grid): - Ranking volatility or algorithm fluctuation - Multiple competitors trading positions - Improvement: stabilize with consistent optimization
Peripheral strength (weak center, strong edges): - Unusual — may indicate address or category issues - Check for GBP location accuracy - Verify centroid of grid matches business location
When scan results contradict what you'd expect from the profile, use these trees to identify root cause.
Business has good reviews (4.5+), correct categories, complete profile, but SoLV under 40%.
Check in order:
1. Duplicate listings — Search for the business name, owner name, old addresses. Duplicate listings split ranking signals. → Use local-seo-audit duplicate listing workflow
2. Category mismatch — Primary category doesn't match the scanned keyword. "Doctor" instead of "Pain management physician." Fix: change primary category to most specific match
3. Address/pin accuracy — GBP pin may be in wrong location, or address doesn't match Google's understanding of the service area. Verify pin placement in GBP
4. Manual penalty/suspension history — Check for past suspensions or guideline violations that may have lingering effects
5. Website disconnect — GBP links to wrong URL, or website has no local signals (no NAP, no schema, no local content). → Use local-seo-audit Section 2
6. New listing — Listings under 6 months old often rank poorly regardless of profile quality. Age is a factor — patience required
7. Competitive density — In saturated markets, a strong profile isn't enough. Need link building, content, and citation advantages. → Use local-competitor-analysis
Business ranks well where it appears, but doesn't appear in most grid points.
Diagnosis: Service area configuration issue. Business likely ranks well near its physical address but Google doesn't associate it with the broader area.
Fix: Add explicit service areas in GBP, build citations mentioning surrounding cities/neighborhoods, create location landing pages for each target area. → Use local-landing-pages and local-citations
Business ranks the same everywhere in the grid — no falloff at distance, but also no boost near the location.
Diagnosis: Relevance problem, not proximity problem. Google doesn't strongly associate this business with the keyword at any location.
Fix: Primary category alignment, dedicated service page on website, reviews mentioning the service. → Use gbp-optimization and review-management
Previous scans showed strong performance, latest scan shows significant decline.
Check in order: 1. GBP changes — Any edits, especially category or address changes, in the last 2 weeks? 2. Unauthorized edits — Did Google or a third party suggest an edit that was accepted? Check GBP edit history 3. New competitor — Pull competitor report to see if a new entrant took position 4. Algorithm update — Check industry forums/Twitter for reported Google local update 5. Website changes — Did the site change CMS, lose pages, break schema, drop HTTPS? 6. Review bombing — Sudden negative reviews can tank rankings. Check review timeline 7. Citation disruption — Data aggregator update pushed wrong info. Check core citations for NAP accuracy
Business ranks well in all directions except one quadrant of the grid.
Diagnosis: A strong competitor owns that geographic zone, OR the business address isn't associated with that area.
Fix: Identify which competitor dominates the weak zone (pull competitor report for that scan). Build citations, content, and reviews referencing the weak area. → Use local-competitor-analysis
Single scans give snapshots. Multiple scans give the full picture.
Run at 3mi, 7mi, and 15mi to see the "falloff curve." Strong at 3mi but gone at 7mi = proximity-dependent ranking. Strong at all radii = genuine authority.
Compare SoLV across keywords. Primary service should be strongest. If a secondary keyword outranks the primary, your primary category or website emphasis may be misaligned.
The most valuable view. Track ARP/SoLV monthly to measure optimization impact. When presenting trends: - Correlate ranking changes with specific actions taken (and log action dates) - Note external factors (algorithm updates, seasonal shifts, competitor moves) - 3+ months of data needed before drawing conclusions
The biggest gap in geogrid reporting: clients don't understand ARP, ATRP, or SoLV. Translate every metric.
Always tie to business outcomes: - "Each 10% increase in SoLV represents approximately X more people seeing your business each month" - "Moving from position 7 to position 3 in the map pack means appearing above the fold — most searchers never scroll past the top 3" - Use competitor names: "Right now, [Competitor] shows up at 85% of these search points. You show up at 14%."
When scanning multiple keywords for the same location: - Compare SoLV across keywords to find strongest/weakest - Primary category alignment usually explains the gap - Create a keyword-SoLV matrix for prioritization - Focus optimization effort on keywords with highest business value AND improvement potential
After analyzing a scan, use the Diagnostic Decision Trees above to identify root cause, then:
| What the Scan Revealed | Next Action | Skill |
|---|---|---|
| Profile issues (category mismatch, incomplete) | Optimize GBP profile | gbp-optimization |
| Weak in specific geographic zones | Build location pages + citations for those areas | local-landing-pages, local-citations |
| Competitor dominating an area | Run competitive analysis on that competitor | local-competitor-analysis |
| Good profile but weak everywhere | Check for duplicates, then audit full local presence | local-seo-audit |
| Need to track improvement over time | Set up recurring scans (same keyword, grid, radius) | Campaign via Local Falcon |
| Client needs to understand this data | Use the Translating Data for Clients section above |
Default next step: Every scan should produce 3-5 specific action items. If you can't produce actions from the scan, you're missing context — run the full audit.
See docs/tool-routing to pick based on what's connected.