Geogrid Analysis Skill — Map Pack Rankings Across Your Entire Service Area

Standard rank tracking tells you that a business ranks #3 for “plumber Denver.” What it doesn’t tell you is that the same business ranks #1 within half a mile of their address, #5 at two miles out, and completely falls off the local pack at three miles — where a competitor with more reviews and a closer address dominates. The geogrid-analysis skill exists because proximity is a ranking factor, and understanding the geographic dimension of your local rankings is the difference between random optimization and strategic intervention.

What This Skill Does

The geogrid-analysis skill equips Claude with the expertise to interpret grid-based rank scan data: a visualization showing where a business ranks for a keyword at each point across a geographic grid covering their service area. The skill runs scans, interprets the ARP/ATRP/SoLV metrics, identifies ranking drop zones and their likely causes, compares current scans to historical data for trend analysis, and generates action plans tied to specific geographic gaps.

Prompt: "Run a geogrid scan for 'plumber near me' in Buffalo centered on 123 Main Street
using a 7x7 grid at 1-mile spacing. Tell me where I'm losing and why."

Skills fired: dispatch → geogrid-analysis + localseodata-tool
Data pulled: geogrid_scan, business_profile (for signal context)

Output: Grid interpretation showing strong rankings (#1-2) within 2 miles north and east,
significant drop-off (#6-10) to the southwest where Ace Plumbing dominates with 3x review
volume and a closer physical address. ARP: 4.2, ATRP: 5.8, SoLV: 58%. The southwest drop
zone is driven by proximity advantage (competitor is closer) and review gap. Top 3 actions:
close the review gap, optimize secondary categories competitor has, consider service area
settings adjustment.

The skill doesn’t just return raw grid data. Claude interprets the pattern, explains what’s causing ranking variations, and connects the geographic analysis to specific optimization actions.

Understanding Geogrid: What You’re Actually Seeing

A geogrid scan runs a rank check for a specific keyword at each point in a geographic grid centered on a location. A 5x5 grid means 25 separate rank checks. A 7x7 grid means 49. Each point returns the ranking position for the target business, creating a spatial map of ranking performance.

Grid size determines resolution. A 3x3 grid provides a quick snapshot with 9 data points — useful for rapid checks 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.

Grid spacing determines geographic coverage. At 0.5-mile spacing, a 5x5 grid covers a roughly 2-mile radius — appropriate for dense urban areas where rankings change block to block. At 1-mile spacing, the same grid covers a 4-mile radius. At 2-mile spacing, you’re mapping an 8-mile radius — appropriate for suburban or rural service areas where businesses compete across larger distances.

The visualization shows ranking position at each grid point. Position 1-3 represents local pack visibility — the user sees you without scrolling. Position 4-7 means you appear in extended local results. Position 8+ or no ranking means invisibility for that point.

The Three Metrics That Matter: ARP, ATRP, and SoLV

Three metrics synthesize geogrid data into actionable numbers. Understanding what each measures — and what it doesn’t — is essential for interpreting results.

ARP (Average Rank Position) is the arithmetic mean of rank positions across all grid points. Lower is better. If a business ranks #1 at 20 points and #5 at 5 points, their ARP reflects that weighted average. ARP provides a single number for overall ranking performance.

The limitation of ARP: it treats all positions equally. Ranking #1 versus #3 matters more than ranking #15 versus #17, but ARP doesn’t capture this. A business that ranks #1 at half their grid points and falls off entirely at the other half could have the same ARP as a business that ranks #7 everywhere.

ATRP (Average True Rank Position) adjusts ARP by penalizing invisibility. When a business doesn’t appear in local results at a grid point, ATRP assigns a penalty position (often #21) rather than ignoring that point. This makes ATRP a harsher metric for businesses with spotty coverage.

ATRP is more useful than ARP for businesses whose core problem is inconsistent visibility. If you rank well where you rank but disappear entirely in large portions of your service area, ATRP captures that problem while ARP might mask it.

SoLV (Share of Local Voice) measures the percentage of grid points where the business appears in positions 1-3 — the local pack. This is the metric that most directly correlates with actual traffic and leads, because the local pack is where clicks happen. Position #1 versus #3 both deliver traffic; position #7 delivers much less.

LocalFalcon research has documented the connection between SoLV and actual lead volume. SoLV improvement correlates with measurable increases in GBP actions (calls, direction requests, website clicks). This makes SoLV the priority metric for most optimization work: improving SoLV from 40% to 60% means appearing in the local pack at 50% more grid points, which translates to more visibility where visibility converts.

Benchmark values vary by category and competitive density. A 70% SoLV might be excellent in a highly competitive urban market and mediocre in a rural area with few competitors. The skill interprets metrics in competitive context, not against arbitrary absolutes.

What Geogrid Reveals That Standard Rank Tracking Misses

Traditional rank tracking gives you a single number: you rank #3 for this keyword. But that ranking was measured from one point — probably your business address or a city center. What happens at other points?

The proximity cliff is the distance from a business’s address at which rankings typically drop. Within that radius, proximity advantage is strong. Beyond it, other factors dominate or competitors with closer addresses take over. The proximity cliff varies by business category (restaurants have tight cliffs; service-area businesses have wider ones) and by competitive density.

Geogrid data reveals your proximity cliff. You might rank #1-2 within one mile and fall to #6-8 at two miles. That pattern tells you that your ranking power drops sharply at the one-mile mark — beyond that, competitors closer to searchers win on proximity.

Geographic ranking patterns inform strategic decisions. If you rank well throughout your service area except one direction where a strong competitor dominates, you might focus marketing there specifically. If your rankings drop universally beyond 1.5 miles, additional physical locations could expand your proximity coverage. If rankings vary by zip code, citation inconsistencies in specific areas might be the cause.

For service-area businesses with no storefront, geogrid data is especially valuable. SABs rely on service area settings and citation geography rather than physical address proximity. Geogrid scans reveal where an SAB is actually visible — which often doesn’t match what the owner assumes based on their service area configuration.

How to Run and Interpret a Geogrid Scan with Claude

The scan workflow starts with prompt parameters: keyword, center location, grid size, and spacing.

Prompt: "Run a 5x5 geogrid scan for 'emergency dentist' centered on 847 Park Ave,
Rochester NY at 1-mile spacing. Interpret the results and tell me what's limiting my
visibility to the east."

Skills fired: dispatch → geogrid-analysis + localseodata-tool
Data pulled: geogrid_scan, business_profile, local_pack (for competitor context)

Claude returns a structured interpretation: the grid visualization described in terms of ranking zones (where you’re strong, where you’re weak), the three key metrics with benchmark context, analysis of ranking drop zones, and prioritized actions tied to the geographic gaps identified.

Grid size selection depends on the question you’re answering. Quick competitive check: 3x3. Standard service area analysis: 5x5. Detailed mapping or large service area: 7x7. More points cost more API credits and take longer, so match grid size to the analysis need.

Grid spacing depends on service area size and competitive density. Urban: 0.5-mile spacing captures block-level variation. Suburban: 1-mile spacing covers typical service areas. Rural or large service area: 2-mile spacing maps broader geographic coverage.

Keyword selection for geogrid matters. Your primary category keyword is the baseline. Secondary keywords for specific services reveal whether ranking patterns vary by service type. Competitor brand keywords (where you rank when someone searches your competitor’s name) can reveal geographic relationship patterns.

Reading Ranking Drop Zones: What Causes What

When rankings drop at specific grid points, the causes vary. The geogrid-analysis skill interprets pattern types and assigns likely causes.

Radial drop-off (rankings decline uniformly in all directions from the address) indicates proximity as the limiting factor. Beyond a certain distance, competitors closer to searchers win. If radial drop-off happens at a short distance, it may indicate weak prominence signals that fail to overcome proximity disadvantages.

Directional drop-off (rankings strong in one direction, weak in another) often indicates a competitor with strong positioning in that direction. A plumber might rank #1 east of downtown but #7 west of downtown because a competitor with more reviews and a west-side address dominates that zone.

Patchy visibility (rankings vary seemingly randomly across the grid) can indicate citation inconsistencies in specific zip codes, inconsistent service area settings, or category mismatches that cause erratic eligibility.

The skill connects pattern observations to likely causes and then to actionable fixes. Radial drop-off with a short proximity cliff suggests prominence improvements (reviews, citations) that extend your competitive radius. Directional drop-off to a strong competitor suggests targeting that competitor’s weaknesses or accepting the geographic limitation. Patchy visibility suggests citation audits for the weak zip codes.

LocalSEOData vs. Local Falcon: Which to Use When

The geogrid-analysis skill routes to different tools depending on the data needed.

LocalSEOData’s geogrid_scan endpoint handles one-time scans and current state analysis. When you need a snapshot of current rankings across a grid, this endpoint delivers. It’s sufficient for initial audits, competitive analysis, and periodic checks.

Local Falcon adds capabilities beyond point-in-time scans. Recurring campaigns run scheduled scans automatically, building a time series of ranking data. Trend reports show how rankings have changed over time at each grid point — essential for measuring optimization impact. Falcon Guard monitors GBP profiles for changes that might affect rankings, alerting you to competitor category changes, new reviews, or profile updates.

The skill knows when to route to each. A prompt like “run a geogrid scan and show me current rankings” routes to LocalSEOData. A prompt like “show me how rankings have changed over the past 6 months” routes to Local Falcon if it’s configured.

For agencies managing ongoing campaigns, Local Falcon’s trend tracking provides the data layer for proving optimization results. For consultants doing initial audits, LocalSEOData’s scan endpoint provides the snapshot needed.

Geogrid for Competitor Analysis

Running geogrid scans for competitors reveals their ranking profile: where they’re strong, where they’re weak, and where their proximity advantage ends.

Your opportunity zones are where competitors drop off. If a competitor dominates within their 1-mile radius but ranks poorly beyond that, the 1-2 mile ring around their address is contested territory. If your prominence signals are stronger there, you can win the fringe areas they can’t reach with proximity alone.

Competitor geogrid analysis also reveals vulnerability to new market entrants. A competitor with a tight proximity cliff and weak visibility beyond 1.5 miles is vulnerable to a new business opening in their weak zone.

Prompt: "Run geogrid scans for my top 3 map pack competitors for 'HVAC repair' in Tampa
and show me where their visibility ends. Where's my opportunity zone?"

Skills fired: dispatch → geogrid-analysis + localseodata-tool
Data pulled: geogrid_scan (x4: target + 3 competitors), local_pack

The output overlays ranking zones for all scanned businesses, identifying areas where no competitor dominates strongly — your opportunity zones — and areas where one competitor is so strong that competing there requires significant investment.

Get Started

Install LocalSEOSkills and configure your LocalSEOData MCP connection (and optionally Local Falcon for trend tracking). Run your first geogrid scan:

Run a geogrid scan for [primary keyword] centered on [your address] using a 5x5 grid at
1-mile spacing. Where am I strong, where am I weak, and what's causing the weak spots?

Claude will return the scan results with metric interpretation, pattern analysis, and prioritized actions. For ongoing optimization, run monthly scans on the same keywords to track whether your fixes are translating to improved geographic coverage.

Learn More

To learn what this skill can do for your local SEO workflow, see the skill overview.