The Geogrid Analysis Skill: See Where You Rank Across Your Entire Service Area
Your client ranks #2 for their primary keyword. You know this because you checked from your office.
What you don’t know — what standard rank tracking can’t tell you — is that they rank #2 within half a mile of their address and completely disappear from the map pack two miles east, where 60,000 potential customers live.
Three competitors dominate that territory. Your client has never appeared there. You’ve been reporting a #2 ranking to a client who is invisible to most of their market.
Local rankings are geographic. You don’t have one position — you have a different position at every point across the service area. Tracking from a single location gives you one data point. A geogrid gives you the complete picture.
What Changes With the Geogrid Analysis Skill
A geogrid scan shows the actual geographic ranking reality: a number at every point across the service area. Where your client appears. Where they don’t. Where competitors dominate.
The geogrid-analysis skill doesn’t just run the scan — it reads the pattern and tells you what it means.
Proximity cliff? Here’s the prominence-building strategy.
Dead zone in the southeast quadrant? A competitor has a proximity advantage there — here’s the counter.
Uniform low ranking across the grid? The problem isn’t geographic, it’s signal strength overall.
The skill translates the 49 numbers into diagnosis and action.
See It Work: Pattern Recognition and Action Plan
Prompt: "Run a 7x7 geogrid for Reliable Plumbing at 2847 Elm Street Charlotte NC
for 'plumber near me' at 1-mile spacing.
Interpret the results and give me the top 3 fixes."
Skills that fire: dispatch → geogrid-analysis + localseodata-tool
Data pulled: geogrid_scan (7×7, 49 points, 1mi spacing, Charlotte NC)
GEOGRID ANALYSIS: Reliable Plumbing — Charlotte Plumber
METRICS
ARP: 5.8 (average rank across 49 grid points)
ATRP: 8.4 (true average — 14 grid points with no appearance)
SoLV: 22% (11 of 49 grid points in top 3)
PATTERN: Proximity Cliff + Southeast Dead Zone
Strong performance (rank 1–3): within 0.75mi of business address
Significant drop (rank 6–15): 1–2mi radius in all directions
Invisible (no appearance): 14 points, concentrated in southeast quadrant
INTERPRETATION
Classic proximity-dominated profile. Strong GBP signals near the address,
insufficient prominence to sustain rankings at distance. Southeast dead zone
is worse than the general proximity cliff — [Competitor A] at [approx. location]
has proximity dominance in that quadrant and is pulling rankings away.
SoLV of 22% means the business is winning roughly 1 in 5 geographic searches.
The top-ranked competitor's SoLV: 61%.
TOP 3 FIXES
1. Review volume campaign — HIGHEST PRIORITY
Current: 52 reviews | [Competitor A]: 189
Reviews build the prominence that sustains rankings at distance.
Target: 120 reviews in 90 days. Expected SoLV gain: +15–20%.
2. Secondary category additions (missing vs. competitors)
"Drain Cleaning Service", "Water Heater Repair", "Emergency Plumber"
Expands relevance at distance for variant queries.
3. Citation push in southeast Charlotte zip codes 28201, 28203, 28204
Builds geographic presence signal in the dead zone area.
Re-scan recommended in 60 days to measure SoLV movement.One prompt. 49 data points interpreted. Pattern identified. Fixes prioritized.
The Three Metrics That Matter
ARP (Average Rank Position)
The arithmetic mean of your rank across all grid points.
Example: If a 25-point grid shows positions [1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,15,15,20,20,NR], your ARP is approximately 7.5.
Limitation: ARP treats all points equally. A business ranking #20 at half the points could have similar ARP to one that appears everywhere at #8.
Use for: Quick summary, month-over-month comparison, initial assessment.
ATRP (Average True Rank Position)
Like ARP, but penalizes non-ranking more heavily. Points where you don’t appear in top 20 are counted as position 21.
Why it matters: ATRP reveals dead zones. A business might have decent ARP but poor ATRP because they disappear entirely in certain areas.
Use for: Identifying coverage problems, comparing businesses with similar ARPs but different patterns.
SoLV (Share of Local Voice)
The percentage of grid points where you rank in the top 3.
Example: If you rank #1-3 at 15 of 25 points, your SoLV is 60%.
Why it matters: Top 3 is where clicks happen. Position #4 might as well be position #10 for most queries. SoLV measures the percentage of your market that sees you prominently.
The revenue connection: SoLV correlates with call volume more directly than ARP. Improving SoLV from 40% to 60% often produces measurable lead increases. SoLV is the metric that connects ranking data to business outcomes.
Use for: Business outcome prediction, competitive benchmarking, ROI measurement, campaign goal-setting.
The Pattern Types the Skill Identifies
Proximity Cliff
Strong rankings near the business address, rapid decline at distance.
What it looks like: Rank 1-3 within 0.5-1 mile, rank 7-12 at 1-2 miles, rank 15+ or not appearing beyond 2 miles.
Root cause: Prominence signals (reviews, citations, authority) insufficient to sustain visibility outside the proximity zone where location closeness helps.
Fix strategy: Build prominence. Review volume campaign. Citation expansion. Local authority links. These signals travel — they improve rankings across the entire grid.
Sector Weakness
Strong in some directions, weak in others. Not a uniform cliff, but directional gaps.
What it looks like: Rank 1-3 north and west, rank 8-15 south and east.
Root cause: Often a competitor with proximity advantage in the weak sector. Sometimes reflects service area concentration or historical customer base.
Fix strategy: Geographic-specific signals. Citations mentioning the weak-sector neighborhoods. Content referencing those areas. Review text mentioning those zones.
Uniform Low
Low rankings across the entire grid without significant pattern variation.
What it looks like: Rank 8-15 everywhere, no strong zones even near the address.
Root cause: Overall signal weakness, not geographic distribution. The profile lacks the prominence to rank anywhere.
Fix strategy: Foundation work before geographic optimization. GBP completeness. Review volume. Category coverage. Build base signals first.
Competitor Anchor
A competitor dominates a specific zone, pulling rankings away in that area.
What it looks like: Your pattern has a hole where a competitor has a physical location advantage.
Root cause: The competitor’s proximity signals overwhelm your prominence signals in that zone.
Fix strategy: Accept the zone as contested rather than owned. Focus resources on expanding dominance in your own strong zones. Build prominence to reduce the size of competitor-dominated zones over time.
Who Uses This and When
New client baseline. Before any optimization begins, understand the geographic reality. Where does the client actually rank? Where are they invisible? This baseline makes progress measurable.
Ranking investigation for stuck or declining clients. When the client says “I used to get more calls from [area],” the geogrid shows whether rankings declined there. The pattern type suggests why.
Pre-campaign and post-campaign benchmarking. Run a geogrid before the review campaign. Run another 60-90 days after. SoLV movement quantifies the impact.
Any competitive market where a single rank number is insufficient. If three competitors are fighting for the same geography, the geogrid shows who owns which territory.
What You Don’t Get Without This Skill
Without the geogrid-analysis skill, Claude can discuss geogrid concepts but cannot:
- Interpret scan data beyond reading the numbers
- Identify pattern types (proximity cliff vs. sector weakness vs. uniform low)
- Connect patterns to root causes
- Generate geography-specific action plans
- Calculate SoLV and explain its significance
- Recommend appropriate fixes based on pattern diagnosis
You’d have a grid of numbers with no synthesis layer. The skill provides the interpretation that makes the data actionable.
The Data Behind the Analysis
The geogrid-analysis skill works with two data sources:
LocalSEOData’s geogrid_scan endpoint — runs real-time scans at specified grid size, spacing, and keyword. Returns position data for each grid point.
Local Falcon integration (local-falcon-tool) — for trend reports (how has the geogrid changed over time) and recurring campaign management (automated monthly scans with alerting).
For a single current-state geogrid, LocalSEOData is sufficient. For historical trends and ongoing monitoring, Local Falcon adds the time dimension.
Get This Skill — It’s Free and Open Source
The geogrid-analysis skill is part of the LocalSEOSkills library. MIT licensed.
Installation:
- Download LocalSEOSkills from GitHub
- Upload to Claude.ai or configure in Claude Code
- Connect LocalSEOData for real-time scans
- Optionally connect Local Falcon for trend reports
- The skill is active immediately
First prompt:
"Run a 7x7 geogrid for [Business Name] at [Address] for '[keyword]' in [city]
at 1-mile spacing. Calculate ARP, ATRP, and SoLV. Identify the pattern
and tell me what to fix."The scan will run, the metrics will calculate, and the pattern will be identified with a geography-specific action plan. That interpretation is what transforms 49 numbers into a strategy.
Skill Documentation
For technical details on how this skill works, what data it pulls, and complete prompt reference, see the full skill documentation.