Local Geo-Relevance Audit

Source: Bill Slawski, SEO by the Sea — 109 articles analyzing Google's local search and maps patents Key Concepts: Structured Information systems, Locally Prominent Semantic Features, Address Completion mechanisms, Geographic Relevance Scoring, Geo-Semantic Index

Philosophy: Think Like Google's Local Algorithm

Google's local search system does not simply match keywords to locations. It builds a geographic entity model of every business — assembling structured data from dozens of sources, analyzing semantic features that make locations distinctive, filling in missing address components, and scoring geographic relevance based on searcher intent and proximity.

Your audit should mirror this process. You are not checking boxes — you are evaluating whether the page gives Google enough structured, semantic, and contextual signal to confidently associate this business with a specific geographic entity.

Before auditing, establish:

• What is the business's primary service area and how many locations does it serve?
• Is this a SAB (Service Area Business) or a storefront with a physical address customers visit?
• What are the 3-5 highest-value local queries this business should rank for?
• Are there geographic naming ambiguities (e.g., "Springfield" exists in 30+ states)?

Nine Signal Categories

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Category 1: NAP Consistency

Patent Concept: Google's Structured Information system assembles business identity from structured sources across the web. Inconsistent NAP (Name, Address, Phone) creates entity ambiguity — Google cannot confidently associate multiple citations with the same business entity.

The consistency rule: Name, Address, and Phone must be identical across:

• The business website (contact page, footer, schema markup)
• Google Business Profile
• All business directory citations (Yelp, Yellow Pages, BBB, industry directories)
• Social media profiles (Facebook, LinkedIn, Instagram)

Common NAP inconsistencies:

• Business name abbreviated on website ("Joe's HVAC") vs. full name on GMB ("Joe's Heating, Ventilation & Air Conditioning Inc.")
• Suite number format: "Suite 200" vs. "#200" vs. "Ste 200"
• Phone number format: (305) 555-1234 vs. 305-555-1234 vs. +13055551234
• Address format: "123 Main St" vs. "123 Main Street"
• Old address from before a business move still appearing on directory sites

Audit method:

1. Document the exact NAP from the Google Business Profile (this is the authoritative source)
2. Search the business name in Google — check top citation sources
3. Use BrightLocal or Moz Local to audit citations systematically
4. Flag every instance where any NAP element differs from the GBP version

Scoring (1-10): 10 = perfectly consistent NAP across all sources. 1 = multiple conflicting NAP formats.

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Category 2: Geographic Entity Signals

Patent Concept: Google builds a geographic entity model for every business, associating it with a specific location in the geographic database. Strong entity signals confirm the business belongs to a specific geographic entity.

Signals that strengthen geographic entity association:

• Coordinates in LocalBusiness schema (geo with latitude/longitude)
• Verified Google Business Profile (the primary geographic entity signal)
• Consistent geographic context across the website (address mentioned in multiple natural contexts)
• Location pages for each physical location (multi-location businesses)
• Geographic metadata in images (EXIF data with location coordinates)
• Google Maps embed on the contact/location page

Check:

• Is there LocalBusiness schema on the homepage and/or contact page?
• Does the schema include geo with precise coordinates?
• Is the Google Maps embed using the business's actual map pin?
• For multi-location businesses: is each location on a separate, unique page?
• Do all location pages have unique, location-specific content (not copies of the same template)?

Scoring (1-10): 10 = full LocalBusiness schema with coordinates, verified GBP, maps embed, location-specific pages.

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Category 3: Locally Prominent Semantic Features

Patent Concept: Google identifies "locally prominent semantic features" — the attributes, landmarks, neighborhoods, and cultural references that make a location distinctive. These are not keyword insertions — they're organic geographic context signals.

Examples of locally prominent semantic features:

• Neighborhood references: "Our Miami office is in Brickell" (specific neighborhood, not just city)
• Landmark proximity: "Located one block from Bayfront Park"
• Local cultural references: Area-specific business climate, seasonal factors, local regulations
• Geographic specificity: service area within specific ZIP codes, not just city
• Local landmarks as wayfinding: "Next to the Whole Foods on Biscayne"

What this is NOT:

• Keyword stuffing of city names
• "We provide [service] in [city], [service] in [city], [service] in [city]..."
• Template content with city name inserted

The test: Would a local resident immediately recognize the geographic references as authentic? If yes, they're genuine locally prominent semantic features. If they read like SEO copy with cities inserted, they're not.

Audit check:

• Does the website content include specific neighborhood names relevant to the business area?
• Are local landmarks or geographic features referenced naturally in the content?
• Does the content reflect local market knowledge (regulations, climate, culture) specific to this area?

Scoring (1-10): 10 = rich locally prominent semantic features throughout content, authentic and specific.

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Category 4: LocalBusiness Schema Completeness

Goal: Maximum machine-readable geographic entity signal through complete structured data.

Required properties for LocalBusiness schema:

{
  "@context": "https://schema.org",
  "@type": "LocalBusiness",
  "name": "Business Legal Name",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "123 Main Street, Suite 200",
    "addressLocality": "Miami",
    "addressRegion": "FL",
    "postalCode": "33132",
    "addressCountry": "US"
  },
  "telephone": "+13055551234",
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": "25.7617",
    "longitude": "-80.1918"
  },
  "url": "https://www.example.com",
  "openingHoursSpecification": [...],
  "hasMap": "https://maps.google.com/?cid=XXXXX",
  "priceRange": "$$",
  "areaServed": ["Miami", "Fort Lauderdale", "Boca Raton"],
  "sameAs": [
    "https://g.page/business-slug",
    "https://www.yelp.com/biz/business-name",
    "https://www.facebook.com/businessname"
  ]
}

Audit checklist:

• [ ] name matches GBP exactly
• [ ] address uses PostalAddress with all components
• [ ] telephone in E.164 format (+1XXXXXXXXXX)
• [ ] geo with precise coordinates (not approximate)
• [ ] openingHoursSpecification for each day
• [ ] hasMap linking to Google Maps listing
• [ ] areaServed for SABs (Service Area Businesses)
• [ ] sameAs linking to GMB, Yelp, Facebook, etc.
• [ ] Schema validates in Rich Results Test

Scoring (1-10): 10 = all required and recommended properties present, validated, matches GBP.

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Category 5: Geo-Relevance Content Signals

Goal: Content on the website that demonstrates genuine connection to the service area — beyond just mentioning city names.

Strong geo-relevance content signals:

• Case studies or testimonials from specific named local neighborhoods or streets
• Blog content addressing local topics (local regulations, weather-related services, area events)
• Staff pages mentioning local team members with location context
• "Areas We Serve" page with specific neighborhoods, cities, and ZIP codes — with unique content per area
• Local news or community involvement referenced
• Local partner businesses or associations mentioned

Weak geo-relevance content signals (these are keyword stuffing, not geo-relevance):

• Same page template with city name swapped: "Plumber in Miami... Plumber in Fort Lauderdale..."
• City name in title tag but no geographic context in content
• A long list of city names as footer text

Scoring (1-10): 10 = rich, authentic, locally-specific content demonstrating genuine market presence.

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Category 6: Geographic Disambiguation

Patent Concept: Google's Address Completion patents show it can fill in geographic gaps — but pages that provide complete geographic context rank better than those relying on inference.

The disambiguation problem:

• "Springfield" exists in 38 US states
• "Portland" exists in Oregon AND Maine
• "Dublin" exists in Ireland, California, Ohio, and more
• "Richmond" exists as a city in Virginia, California, and British Columbia

Check:

• Does the website content make it unambiguous which specific geographic location this business serves?
• Is the state/province/country always mentioned alongside the city?
• For city-level disambiguation: are neighborhood or ZIP codes included?
• For multi-state SABs: is the service area clearly delineated to avoid confusion?

Disambiguation techniques:

• Always mention state with city: "Miami, Florida" not just "Miami"
• Include ZIP codes in address schema
• Reference state-specific regulations, licensing, or context
• Use the full geographic name in structured data

Scoring (1-10): 10 = zero geographic ambiguity possible across all signals.

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Category 7: Local Citation and Link Signals

Goal: Authority signals from locally relevant sources confirm the business's geographic association.

High-value local citation sources:

• Local Chamber of Commerce membership directory
• Local Better Business Bureau listing
• City/county government business license directories
• Local newspaper mentions or features
• Local industry associations (e.g., Miami chapter of NARI for contractors)
• Area-specific review sites (neighborhood apps, local Facebook groups archived)

High-value local link signals:

• Links from local news sites (miamiherald.com, local.com)
• Links from area universities or community organizations
• Links from complementary local businesses (e.g., a contractor linked from a local building supply store)
• Local business association websites

Low-value (or negative) citation signals:

• Low-quality national directories with no local specificity
• Purchased citations on irrelevant directories
• Citations where NAP doesn't match (actually hurt consistency)

Audit method:

1. Check major local citation sources (Google your business type + city to find where competitors appear)
2. Verify presence and NAP accuracy on each
3. Identify gaps: what authoritative local sources don't include you?
4. Build a priority citation list

Scoring (1-10): 10 = listed on all major local authority sources with consistent NAP.

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Category 8: Review Signal Optimization

Goal: Consistent, authentic review signals across platforms that reinforce geographic relevance and service quality.

Review signals Google uses:

• Total review count across platforms
• Average rating
• Review recency (fresh reviews signal active business)
• Review content mentioning local context
• Response rate and quality (business owner responses to reviews)
• Platform authority (Google reviews > Yelp > lesser directories)

Local review content that strengthens geo-signals:

• Reviewer mentions specific neighborhood or local landmark in review
• Review mentions a locally-recognizable context ("I found them through the Brickell neighborhood Facebook group...")
• Review mentions local staff by name

Review audit checks:

• Google Business Profile: review count, average rating, recency of reviews
• Yelp listing: claimed? Rating? Recency?
• Industry-specific directories: BBB, Angi, HomeAdvisor, etc.
• Response rate: what % of reviews have a business owner response?

Red flags:

• All reviews posted in same month (artificial review campaign)
• Same generic language across multiple reviews
• Reviews from accounts with no other activity
• Zero owner responses (signals disengaged business)

Scoring (1-10): 10 = 50+ reviews on Google, 4.5+ average, recent, owner responses on all.

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Category 9: SAB vs. Storefront Signal Differentiation

Patent Concept: Service Area Businesses (SABs) and storefront businesses need different structured data treatment. Misclassification creates ranking signals that don't match the business model.

SAB (Service Area Business):

• Does not serve customers at its address (plumbers, cleaners, movers)
• In GBP: address is hidden, service areas are specified
• In LocalBusiness schema: use areaServed with service area list; do NOT use hasMap pointing to a physical address customers visit
• Service area should be explicitly defined with city/ZIP lists

Storefront Business:

• Customers come to the location (restaurants, retail stores, dental offices)
• In GBP: address is visible
• In LocalBusiness schema: full address and geo coordinates
• hasMap links to the business's map pin

Common errors:

• SAB showing a physical address in GBP (they receive visits to a home/warehouse — creates review location confusion)
• Storefront business hiding its address (loses foot-traffic signals)
• SAB using a virtual office address for location authority (violates GBP terms, triggers suspension risk)

Check:

• Does the GBP classification match the actual business model?
• Does the schema reflect SAB vs. storefront correctly?
• For SABs: is areaServed populated with the actual service area?
• For storefronts: is the address visible and accurate?

Scoring (1-10): 10 = correctly classified in all signals; schema and GBP match business model; no mixed signals.

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Scoring Summary

Signal Category	Score (1-10)
NAP Consistency	/10
Geographic Entity Signals	/10
Locally Prominent Semantic Features	/10
LocalBusiness Schema Completeness	/10
Geo-Relevance Content Signals	/10
Geographic Disambiguation	/10
Local Citation & Link Signals	/10
Review Signal Optimization	/10
SAB vs. Storefront Signals	/10
TOTAL	/90

Score Interpretation:

• 72-90: Strong local geo-relevance — competitive in local pack
• 54-71: Moderate — NAP consistency and schema are usually the quickest wins
• 36-53: Weak — foundation signals missing; start with GBP verification and schema
• Below 36: Critical — business is not sending coherent geographic signals to Google

Priority Action Order for Local SEO

1. Verify Google Business Profile (prerequisite — nothing else works without this)
2. Fix NAP consistency (fastest authority win across the citation network)
3. Add complete LocalBusiness schema with coordinates and sameAs links
4. Build local citations on major authority directories
5. Add locally prominent semantic features to website content
6. Request reviews systematically (email, QR code at point of service)
7. Build local links from chamber, associations, local news
8. Audit disambiguation — ensure geographic context is unambiguous everywhere