Local SEO Automation with Claude Code

Key Takeaways

• GBP profiles require constant maintenance: NAP inconsistencies across 50+ locations silently suppress map pack rankings, and manual audits don't scale past a handful of clients
• Citation drift happens without intervention: A business that changes its phone number or address creates citation conflicts across dozens of directories within weeks
• LocalBusiness schema is underdeployed: Most local businesses have no JSON-LD schema at all — generating it at scale is a direct competitive advantage for your clients
• Map pack tracking is location-specific: A dentist in Austin ranks differently in different ZIP codes; tools that ignore geo-grid positioning miss this entirely
• Claude Code handles multi-client orchestration: One CLAUDE.md config file per client, parallel audits across accounts, structured output for every deliverable
• See how the Claude Code AI SEO skills library covers the technical building blocks this workflow depends on

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Local search is where most purchase decisions happen. Google and BrightLocal have consistently reported that a large share of searches have local intent, and map pack visibility for local business clients is not a vanity metric — it directly determines whether the phone rings.

Managing that visibility at scale is a different problem. An agency running 50 local accounts needs to audit 50 Google Business Profiles, monitor citations across dozens of directories, generate schema markup for every location page, and track map pack positions across target keywords and cities. Doing that manually means it either doesn't get done, or it gets done poorly.

Claude Code changes the math. This guide shows you the exact workflows: how to structure your project for multi-client local SEO, automate GBP audits, monitor citations, generate LocalBusiness schema at scale, and track map pack positions without a spreadsheet in sight.

Why Local SEO Agencies Need Automation

Local SEO agencies managing 50 or more Google Business Profiles face a compounding oversight problem. Each profile requires regular auditing for NAP (name, address, phone) consistency, category accuracy, attribute completeness, and photo freshness. Manual review across a large client roster is not just slow — it creates blind spots. Errors that should take minutes to catch persist for months because no one has bandwidth to check every account consistently.

Managing multiple client accounts is consistently reported as one of the top operational challenges for local SEO agencies. The agencies that grow aren't hiring faster — they're systematizing.

The core problem with manual local SEO management:

• GBP profiles get edited by clients (or Google's automated suggestions get accepted accidentally)
• Citation data drifts as businesses change addresses, phone numbers, or hours
• Schema markup gets skipped because it's time-consuming to generate per location
• Map pack rank checks happen inconsistently, so ranking drops go unnoticed for weeks

Claude Code addresses each of these with repeatable, automatable workflows. You write the logic once, run it across all clients.

The Multi-Client Project Structure

Before anything else, set up a directory structure that scales:

local-seo/
├── clients/
│   ├── client-a/
│   │   ├── CLAUDE.md           # Client context + instructions
│   │   ├── config.json         # GBP place IDs, NAP data, target keywords
│   │   ├── data/               # Fetched GBP data, citation exports, rank data
│   │   └── reports/            # Generated audit outputs
│   ├── client-b/
│   │   └── ...
├── scripts/
│   ├── gbp_audit.py            # GBP data fetcher via Places API
│   ├── citation_check.py       # Citation consistency checker
│   ├── schema_gen.py           # LocalBusiness JSON-LD generator
│   └── rank_tracker.py         # Map pack position tracker
└── templates/
    └── local_business_schema.json

Each client's CLAUDE.md tells Claude Code exactly who this client is: industry, target service areas, competitors, and what "correct" NAP data looks like.

# Open Claude Code in a specific client's directory
cd local-seo/clients/client-a
claude

From there, Claude Code has access to the client config and all their data.

GBP Audit Automation

Google Business Profile (GBP) audits check whether a business's public-facing information is accurate, complete, and consistent with what appears on the website. For agencies, this means verifying NAP data, primary and secondary categories, business attributes, hours, photos, and the Q&A section — across every client account, every month. Claude Code can pull GBP data via the Google Places API and flag every discrepancy automatically.

What to Check in Every GBP Audit

Field	What to Verify
Business Name	Matches website exactly (no keyword stuffing)
Address	Formatted consistently with website and citations
Phone Number	Local number, matches website header/footer
Primary Category	Most specific accurate category available
Secondary Categories	All relevant services represented
Hours	Current, including holiday hours
Website URL	UTM parameters if tracking agency traffic
Attributes	All applicable attributes enabled (wheelchair access, etc.)
Photos	Recent exterior, interior, team photos uploaded
Q&A	No unanswered questions, no spam

Setting Up the GBP Data Fetcher

import googlemaps
import json

client = googlemaps.Client(key='YOUR_PLACES_API_KEY')

def fetch_gbp_data(place_id):
    result = client.place(
        place_id,
        fields=[
            'name', 'formatted_address', 'formatted_phone_number',
            'website', 'opening_hours', 'types', 'rating',
            'user_ratings_total', 'photos', 'url'
        ]
    )
    return result.get('result', {})

def audit_gbp(place_id, expected_nap):
    live_data = fetch_gbp_data(place_id)
    issues = []

    if live_data.get('name') != expected_nap['name']:
        issues.append(f"Name mismatch: got '{live_data.get('name')}', expected '{expected_nap['name']}'")

    if live_data.get('formatted_phone_number') != expected_nap['phone']:
        issues.append(f"Phone mismatch: got '{live_data.get('formatted_phone_number')}', expected '{expected_nap['phone']}'")

    # Save to data directory for Claude Code to analyze
    with open('data/gbp_audit.json', 'w') as f:
        json.dump({'live': live_data, 'issues': issues}, f, indent=2)

    return issues

Run it for a client:

python3 scripts/gbp_audit.py --place-id ChIJN1t_tDeuEmsRUsoyG83frY4 --config clients/client-a/config.json

Then inside Claude Code:

Read data/gbp_audit.json and generate a GBP audit report. Flag every issue with severity (Critical, Warning, Info). Include recommended fixes for each issue. Format as markdown.

Claude Code produces a structured report in seconds. For 20 clients, run the fetcher in parallel and get 20 reports without touching a dashboard.

Local Citation Monitoring

Local citations are mentions of a business's name, address, and phone number across directories, data aggregators, and third-party websites. Citation consistency is a confirmed local ranking factor — NAP discrepancies across sources confuse search engines and suppress map pack visibility. Monitoring citations manually across 50+ directories for 50+ clients is not viable. Claude Code automates both the data collection and the discrepancy detection.

The primary citation sources that carry the most weight (Whitespark Local Citation Finder research, 2025):

1. Data aggregators: Data Axle, Neustar Localeze, Foursquare
2. Core directories: Yelp, Apple Maps, Bing Places, Yellow Pages
3. Industry-specific directories (e.g., Healthgrades for medical, Avvo for legal, Houzz for home services)
4. Chamber of commerce and local business associations

Citation Audit Workflow

Export citation data from a tool like BrightLocal, Whitespark, or Moz Local as CSV. Drop the export in the client's data directory and let Claude Code do the analysis.

# Example directory structure after export
clients/client-a/data/
├── citations_export.csv    # BrightLocal citation export
└── source_of_truth.json    # Correct NAP data from config

Inside Claude Code:

Read citations_export.csv and source_of_truth.json.

Find all citations where:
1. The business name doesn't match exactly
2. The address differs from the source of truth
3. The phone number is different or missing
4. The website URL is missing or incorrect

Group findings by severity. For each discrepancy, note which directory it's on and what the correct value should be. Output as a markdown table sorted by directory authority.

Claude Code returns a prioritized fix list. High-authority directories (Yelp, Apple Maps) get fixed first.

Detecting Citation Drift Over Time

Citation drift happens after a business moves, changes its phone number, or rebrands. Run the audit on a monthly schedule and compare against the previous month's output.

Compare citations_jan_2026.csv with citations_feb_2026.csv.

Identify:
1. New citation discrepancies that weren't present last month
2. Citations that were fixed (discrepancy resolved)
3. New directory listings (positive)
4. Listings that disappeared

Summarize what changed and what needs attention.

This gives you a change log for every client without manually reviewing any directory.

LocalBusiness Schema Generation at Scale

LocalBusiness schema is JSON-LD structured data that tells search engines exactly what a business is, where it is, and what it offers. Google uses this data for Knowledge Panels, rich results, and increasingly for AI-generated local answers. Despite being a high-impact technical SEO task for local businesses, most location pages have no schema at all. Claude Code generates production-ready LocalBusiness JSON-LD from a simple config file — for every location, in one pass.

The Location Config File

Each client keeps a locations array in their config:

{
  "business_name": "Greenway Dental",
  "business_type": "Dentist",
  "parent_organization": "Greenway Dental Group",
  "locations": [
    {
      "location_id": "austin-main",
      "name": "Greenway Dental Austin",
      "street_address": "1234 South Congress Ave",
      "city": "Austin",
      "state": "TX",
      "zip": "78704",
      "phone": "+15124561234",
      "url": "https://greenwaydental.com/austin/",
      "latitude": 30.2453,
      "longitude": -97.7503,
      "hours": {
        "Monday": "08:00-17:00",
        "Tuesday": "08:00-17:00",
        "Wednesday": "08:00-17:00",
        "Thursday": "08:00-17:00",
        "Friday": "08:00-15:00"
      },
      "services": ["General Dentistry", "Cosmetic Dentistry", "Orthodontics"],
      "price_range": "$$",
      "image_url": "https://greenwaydental.com/images/austin-office.webp"
    }
  ]
}

Generating Schema for Every Location

python3 scripts/schema_gen.py --config clients/client-a/config.json --output clients/client-a/data/schema/

The script loops through every location and generates a JSON-LD file. Then inside Claude Code:

Read all JSON files in clients/client-a/data/schema/.

For each location:
1. Validate the schema structure against Schema.org LocalBusiness spec
2. Check that all required fields are present (name, address, telephone, url, geo, openingHours)
3. Flag any missing recommended fields (image, priceRange, servesCuisine for restaurants, etc.)
4. Generate the final <script type="application/ld+json"> embed code for each location page

Output one markdown file per location with the embed code ready to paste.

For a client with 12 locations, this produces 12 validated schema blocks in a single pass.

Sample Output

{
  "@context": "https://schema.org",
  "@type": "Dentist",
  "name": "Greenway Dental Austin",
  "image": "https://greenwaydental.com/images/austin-office.webp",
  "url": "https://greenwaydental.com/austin/",
  "telephone": "+15124561234",
  "priceRange": "$$",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "1234 South Congress Ave",
    "addressLocality": "Austin",
    "addressRegion": "TX",
    "postalCode": "78704",
    "addressCountry": "US"
  },
  "geo": {
    "@type": "GeoCoordinates",
    "latitude": 30.2453,
    "longitude": -97.7503
  },
  "openingHoursSpecification": [
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": ["Monday", "Tuesday", "Wednesday", "Thursday"],
      "opens": "08:00",
      "closes": "17:00"
    },
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": ["Friday"],
      "opens": "08:00",
      "closes": "15:00"
    }
  ],
  "parentOrganization": {
    "@type": "Organization",
    "name": "Greenway Dental Group"
  }
}

Schema generation that happens location by location in manual workflows can run across all locations in one pass with this setup.

Map Pack Position Tracking

Map pack tracking measures where a local business appears in Google's local 3-pack for target keywords across different geographic points within a service area. A business ranking #1 in one ZIP code may not appear at all two miles away. Standard rank tracking tools miss this geo-grid reality. Claude Code integrates with SERP APIs to pull location-specific map pack data and analyze position patterns across an entire service area.

The Geo-Grid Problem

A dentist in Austin targeting "emergency dentist Austin" doesn't have one ranking. They have hundreds — one for each point on the geographic grid that Google evaluates. DataForSEO's Local Pack API and SerpApi's Google Maps API both support lat/long parameters so you can simulate searches from specific locations.

import requests
import json

def check_map_pack_position(keyword, lat, lng, place_id):
    """
    Check where a specific GBP listing appears in the local pack
    for a given keyword and geographic coordinate.
    """
    params = {
        'engine': 'google_maps',
        'q': keyword,
        'll': f'@{lat},{lng},14z',
        'api_key': 'YOUR_SERPAPI_KEY'
    }

    response = requests.get('https://serpapi.com/search', params=params)
    results = response.json()

    local_results = results.get('local_results', [])
    for rank, result in enumerate(local_results, start=1):
        if result.get('place_id') == place_id:
            return rank

    return None  # Not in top results

def run_geo_grid_audit(config_path, keyword, grid_points):
    with open(config_path) as f:
        config = json.load(f)

    rankings = []
    for point in grid_points:
        rank = check_map_pack_position(
            keyword,
            point['lat'],
            point['lng'],
            config['place_id']
        )
        rankings.append({
            'lat': point['lat'],
            'lng': point['lng'],
            'rank': rank,
            'keyword': keyword
        })

    with open('data/geo_grid_rankings.json', 'w') as f:
        json.dump(rankings, f, indent=2)

    return rankings

Analyzing Position Data in Claude Code

After running the geo-grid audit, Claude Code interprets the results:

Read data/geo_grid_rankings.json.

This is map pack ranking data for "emergency dentist Austin" across a 5x5 grid of coordinates.

Analyze:
1. What's the average ranking across all grid points?
2. Where are the strongest positions (rank 1-3)? Describe the geographic pattern.
3. Where does the listing drop out of the top 3? Any visible pattern?
4. Which grid points show no ranking at all?
5. What does this suggest about where to focus local SEO effort (citations, content, link building)?

Format findings as an executive summary + a ranked action list.

The output gives you a geographic picture of where the client is winning and losing — the kind of analysis that normally requires a dedicated geo-grid tool like Local Falcon or BrightLocal's Grid Tracker.

Monthly Tracking Protocol

# Pull geo-grid data for all target keywords monthly
python3 scripts/rank_tracker.py \
  --config clients/client-a/config.json \
  --keywords "emergency dentist austin,dentist near me,dental implants austin" \
  --grid-size 5x5 \
  --output clients/client-a/data/rankings/march-2026/

Then in Claude Code:

Compare rankings/february-2026/ with rankings/march-2026/ for all keywords.

Show month-over-month position changes per keyword and per grid point.
Flag any keyword where average position dropped more than 1 position.
Flag any keyword where we entered or fell out of the top 3.
Generate a client-ready summary paragraph explaining what changed and why.

With this setup, a monthly ranking summary for one client is a script run plus a Claude Code analysis prompt. For 20 clients, run the fetcher scripts in parallel and review all reports in a single session.

FAQ

How does Claude Code access Google Business Profile data?

Claude Code doesn't connect directly to GBP's management interface. The workflow uses the Google Places API to fetch publicly visible business data (name, address, phone, hours, categories, photos) and the My Business Business Information API (for verified owners) to access additional fields. You write Python scripts to fetch and save this data as JSON, then Claude Code analyzes it. The Claude Code for SEO product page covers the full authentication setup.

Can Claude Code detect when a GBP listing gets suspended?

Not directly through the Places API, because suspended listings stop returning data. The detection method is indirect: if a place ID that previously returned results returns no data, flag it for manual review. Set up a weekly fetch script and pipe any missing responses to a monitoring alert. Claude Code can write this monitoring script for you in one prompt.

What citation tools work best with this workflow?

BrightLocal and Whitespark both export citation data as CSV, which Claude Code reads without any preprocessing. Moz Local exports to CSV as well. The key is getting a consistent export format so the analysis prompt works across all clients without adjustment. Read more about the full SEO workflow setup in the SEO Command Center guide.

How accurate is geo-grid map pack tracking via SERP APIs?

SerpApi and DataForSEO both return real Google Maps results for the coordinates you specify. The results reflect what a user in that geographic location would see. The limitation is that Google personalizes results based on user history and device — API results represent the "neutral" view, not a logged-in user's experience. For tracking purposes, the neutral view is the right baseline.

How long does it take to set this up for a new client?

Initial setup involves creating the config file with place IDs and NAP data, adding the GBP service account, and confirming the first data fetch works. After that, monthly maintenance is running the fetch scripts and reviewing Claude Code's analysis reports. The time per client drops significantly once the scripts are in place.

Which local business types benefit most from this workflow?

Multi-location businesses get the most out of this workflow: dental groups, law firms, medical practices, franchise locations, and home services companies with multiple service areas. Single-location businesses benefit from the schema generation and citation monitoring, but the workflow pays off most clearly when you're managing 10 or more locations under one client account. The Claude Code AI SEO skills library includes pre-built skills for the most common local SEO tasks.