How to Run a Geogrid Scan with Claude (With Real Output Examples)

A dentist in Chicago has a strong GBP profile: complete categories, 150 reviews at 4.7 stars, all attributes filled out. They should be ranking well. But they’re getting inconsistent results — some searches show them in the top 3, others show them on page 2. What’s happening?

A geogrid scan reveals what standard rank tracking hides: the geographic dimension of local rankings. That dentist might rank #1 within half a mile of their office and completely fall off the map at two miles — where a competitor with a closer address dominates. Understanding this pattern changes the optimization strategy entirely.

What a Geogrid Scan Shows (And Why You Need One)

Traditional rank tracking tells you a single number: you rank #3 for “dentist near me.” But that ranking was measured from one location, usually your business address or city center. What happens when the searcher is two miles away? Five miles?

Proximity is a local ranking factor. Google prefers businesses closer to the searcher. A geogrid scan checks rankings at multiple geographic points arranged in a grid, revealing how proximity affects your visibility across your entire service area.

The output is a ranking at each grid point: 49 separate rank checks for a 7x7 grid. These individual positions aggregate into three key metrics:

ARP (Average Rank Position): The arithmetic mean of your rank across all grid points. Lower is better.

ATRP (Average True Rank Position): Like ARP, but assigns a penalty rank (usually 21) to grid points where you don’t appear at all. This penalizes invisibility more harshly.

SoLV (Share of Local Voice): The percentage of grid points where you appear in positions 1-3 — the local pack. This is the metric that most directly correlates with traffic, because the local pack is where clicks happen.

What You’ll Need

LocalSEOSkills installed with LocalSEOData MCP connection configured. For trend tracking over time, configure Local Falcon as well.

Information needed: business name and address, target keyword, grid size (3x3, 5x5, or 7x7), grid spacing (distance between points), and location/city context.

Choosing Your Grid: Size, Spacing, and Keywords

Grid size determines resolution. A 3x3 grid gives you 9 data points — a quick check but limited detail. A 5x5 grid with 25 points is the standard for service area analysis. A 7x7 grid with 49 points provides detailed mapping for complex competitive situations or large service areas.

Spacing determines geographic coverage. At 0.5-mile spacing, a 5x5 grid covers roughly a 2-mile radius — appropriate for dense urban areas. At 1-mile spacing, the same grid covers a 4-mile radius. At 2-mile spacing, you’re mapping an 8-mile radius for suburban or rural service areas.

Keyword selection matters because ranking profiles can differ significantly by keyword. Start with your primary category keyword (“dentist near me”), then add service-specific keywords (“cosmetic dentist,” “emergency dentist”) to see if patterns vary.

The Prompt

Run a geogrid scan for Lakeside Family Dental at 2847 N Lincoln Ave, Chicago IL.
Scan for "dentist near me" using a 7x7 grid with 0.5-mile spacing.
Give me the ARP, ATRP, and SoLV, interpret the ranking pattern,
and tell me the top 3 things to fix.

This prompt specifies all parameters: business identifier, keyword, grid size, spacing, and the analysis depth you want.

What Happens When You Run It

The dispatch skill routes to geogrid-analysis + localseodata-tool. LocalSEOData’s geogrid_scan endpoint runs with parameters: business identifier, keyword “dentist near me”, grid_size 7, spacing 0.5mi, location Chicago IL.

Claude receives 49 data points — the rank at each grid coordinate. The geogrid-analysis skill then:

  1. Calculates ARP, ATRP, and SoLV from raw data
  2. Identifies the ranking pattern type (proximity cliff, uniform low, sector weakness, etc.)
  3. Connects the pattern to likely root causes
  4. Generates prioritized actions targeting those causes

Reading Your Results

The Three Metrics

SUMMARY METRICS
ARP: 4.2 (average rank across all 49 grid points)
ATRP: 6.8 (true average penalizing 11 grid points where business doesn't appear)
SoLV: 38% (19 of 49 grid points in top 3)

ARP of 4.2 looks decent in isolation. But ATRP of 6.8 reveals the problem: there are 11 grid points where the business doesn’t appear at all. Those invisible points drag down the true average significantly.

SoLV of 38% means the business appears in the local pack at only 38% of the grid — less than 2 out of every 5 potential searcher locations. For a competitive market like Chicago dentistry, this leaves significant traffic on the table.

The Four Ranking Pattern Types

Proximity Cliff: Strong rankings near the business address, dramatic drop-off beyond a certain distance. This indicates the business has good profile signals but lacks the prominence (reviews, citations, links) to sustain rankings at distance.

RANKING PATTERN: Proximity Cliff
Strong performance (rank 1-2) within 0.75 miles of business address.
Significant drop (rank 8-20+) beyond 1 mile in all directions.
11 grid points with no appearance (beyond 1.25 miles east and south).

Uniform Low: Consistently poor rankings across the entire grid. This indicates fundamental profile problems: category mismatch, thin GBP profile, minimal reviews. Distance doesn’t matter because base signals are weak everywhere.

Sector Weakness: Strong in some directions, weak in others. Often indicates a competitor with a closer physical address dominating a specific zone, or citation inconsistencies affecting specific zip codes.

Patchy Inconsistent: Rankings vary seemingly randomly across the grid. Often indicates citation inconsistencies, inconsistent service area settings, or category mismatches causing erratic query eligibility.

Example Output Walkthrough

INTERPRETATION
This is a classic proximity-dominated ranking profile. The business has strong
GBP signals (good completeness, reviews, categories) but lacks the prominence
signals that sustain rankings at distance from the address.

At 1+ miles, 3 competitors with more reviews and stronger citation profiles
become proximity-dominant. The eastern drop-off is steeper — likely a competitor
at approximately [Competitor Address] with a significant proximity advantage
in that zone.

GRID DETAIL
NW: #3  N: #1   NE: #2   — Strong core zone
W:  #2  C: #1   E: #4    — Center dominance, east starting to slip
SW: #5  S: #3   SE: #8   — South weakening
[2nd ring...]
[3rd ring: showing #8-15 positions, several "NR" (not ranking)]

The eastern drop corresponds to [Competitor Name]'s coverage zone.

Translating Findings into Action

For a proximity cliff pattern, the strategy focuses on prominence building — extending competitive range beyond the natural proximity advantage:

TOP 3 ACTIONS

1. Review volume and velocity campaign
   Current: 67 reviews | Competitor average at 1+ miles: 180+
   Target: 120 reviews in 90 days
   Impact: ATRP improvement of ~1.5 points estimated, SoLV +12-18%
   Why: Reviews are the primary prominence signal that sustains ranking at distance

2. Citation audit and cleanup
   Inconsistent NAP detected in 6 directories (old suite number variation)
   Fixing aggregator-level data will strengthen prominence signal
   Impact: Supports sustained ranking at distance, particularly in eastern zone
   Why: Citation inconsistency weakens entity signal Google uses for prominence

3. Secondary category additions
   "Pediatric Dentist", "Cosmetic Dentist", "Teeth Whitening Service"
   not claimed — these expand query surface
   Impact: Improves ranking for variant keywords at all grid distances
   Why: Category expansion increases query eligibility across the grid

Each action ties back to the diagnosed pattern. A proximity cliff requires prominence building, not profile tweaking.

LocalSEOData vs. Local Falcon: Which to Use

LocalSEOData’s geogrid_scan is best for current-state analysis: what does the ranking grid look like right now? Use this for initial audits, one-time competitive analysis, and baseline establishment.

Local Falcon adds capabilities for ongoing management: recurring campaigns that run scheduled scans automatically, trend reports showing how rankings have changed over time, and Falcon Guard for automated GBP change monitoring.

For agencies managing ongoing campaigns, Local Falcon’s trend tracking provides the data for proving optimization results. For consultants doing initial audits, LocalSEOData covers the need.

To specify in your prompt:

# Current-state scan (uses LocalSEOData)
Run a geogrid scan for [Business] for [keyword]...

# Trend analysis (routes to Local Falcon if configured)
Show me how geogrid rankings have changed over the past 90 days for [Business]...

Setting Up a Recurring Campaign

For ongoing monitoring, configure a Local Falcon campaign:

Set up a weekly Local Falcon geogrid campaign for [Business Name] tracking
"dentist Chicago" and "emergency dentist Chicago" with a 5x5 grid at 1-mile spacing.

Weekly scans build a trend dataset. After 90 days, you have 12 data points per keyword showing how rankings evolved. The trend report reveals whether optimization efforts translate to geographic expansion.

What trends indicate:

  • SoLV increasing: Geographic coverage expanding. Efforts are working.
  • SoLV stable: Holding position but not gaining ground.
  • SoLV declining: Competitors gaining, or your signals weakening.
  • Sector-specific changes: A competitor entered the market in a specific direction.

Next Steps After Your First Scan

The geogrid establishes your geographic ranking baseline. Based on findings:

For proximity cliff patterns: Launch review generation campaign, run citation audit, consider secondary category additions.

For uniform low patterns: Run a full GBP audit first — fundamental profile issues need addressing before geographic optimization matters.

For sector weakness patterns: Investigate the competitor dominating that zone. Run geogrid on them to see where their coverage ends. That’s your opportunity zone.

Schedule a re-scan in 60 days after implementing changes. Compare SoLV improvement to validate that fixes are translating to expanded geographic coverage.