Local Business Intelligence Suite

"God Mode for Local SEO" by John Rippy | johnrippy.link

🏆 2025 Zapier Automation Hero of the Year — Project Phoenix: A 95-step AI sales pipeline cutting development time by 50%. Read more →

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Stop Paying for 10+ Separate Local SEO Tools

You're currently paying for: BrightLocal ($39/mo), LocalFalcon ($29/mo), GatherUp ($99/mo), SEMrush ($129/mo), Moz Local ($14/mo), Yext ($499/mo), ReviewTrackers ($49/mo), and a dozen browser extensions. What if one tool did it all?

The Local Business Intelligence Suite combines 19 specialized modules into one powerful actor:

Google Business Profile scraping (reviews, posts, photos, Q&A, hours, attributes)
Multi-platform review aggregation with AI sentiment analysis (Google, Yelp, Facebook, TripAdvisor)
Local SERP ranking with map grid analysis (like LocalFalcon, but without the $29/mo)
Citation auditing across 20+ directories (like Moz Local, but on-demand)
Business enrichment with contact discovery (like ZoomInfo, but pay-per-use)
Tech stack detection for 50+ trackers and pixels
Reddit intelligence for reputation and local discussions
Market Intelligence powered by US Census, SBA, and County Business Patterns data
NEW: Smart Reputation Aggregator - auto-detects business type and scrapes relevant platforms
And 9 more specialized modules...

Pay only for what you use. No monthly subscriptions. No minimum commitments.

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19 Intelligence Modules — Every Local SEO Task in One Place

Core Intelligence (v1.0)

Advanced Intelligence (v1.2-1.3)

Market Intelligence (v1.4-1.5)

Reputation Aggregator (v1.6) - NEW!

Platforms by Business Type:

All-In-One

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Why Agencies Choose This Over Traditional Tools

1. Pay-Per-Use, Not Per-Month

Traditional tools: $500-$2,000/month for your agency.

This actor: Pay per task. A full business audit costs ~$2. A GBP scrape is ~$1. Track rankings for 5 keywords: ~$0.50.

Run 100 audits/month and still pay less than one BrightLocal subscription.

2. All Data in One Place

Stop logging into 8 different dashboards. Get GBP data, reviews, rankings, citations, backlinks, social metrics, and market data in one JSON response.

Feed it directly into your CRM, reporting tool, or AI assistant.

3. Automation-Ready with Webhooks

Built-in webhook support for:

Zapier — Trigger workflows when audits complete
Make (Integromat) — Build complex automations
n8n — Self-hosted automation
Custom endpoints — Send to your own systems

Just add your webhook URL and results automatically POST to your endpoint.

4. Census-Powered Market Intelligence

No other Apify actor gives you US Census Bureau data for local SEO:

Population demographics by ZIP code
Median household income
County Business Patterns (competitor counts by industry)
5-year market projections
"Dollars up for grabs" calculations for new market entrants

This is the data enterprise consultants pay $10,000+ for in IBISWorld reports.

5. AI Assistant Integration (MCP Server)

This actor functions as an MCP Server for Claude and other AI assistants:

"What's the Google rating for Joe's Pizza in NYC?"
"How does my client compare to their top 3 competitors?"
"Run a full audit and tell me the top 5 priorities"

Your AI assistant becomes a local SEO expert.

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Quick Start Examples

Example 1: Full Business Audit ($2)

{
"task": "full_audit",
"businessName": "Joe's Pizza",
"city": "New York",
"state": "NY",
"website": "https://joespizza.com",
"keywords": ["pizza near me", "best pizza nyc"],
"competitors": ["Ray's Pizza", "Famous Original Pizza"]
}

Returns: GBP profile, reviews across platforms, local rankings, website SEO score, competitor comparison, overall score (0-100), and prioritized recommendations.

Example 2: Market Intelligence (Census Data)

{
"task": "market_intelligence",
"businessName": "Joe's Pizza",
"city": "New York",
"state": "NY",
"zipCode": "10001"
}

Returns: Population demographics, median income, competitor counts from County Business Patterns, industry statistics from SBA, 5-year market projections, "dollars up for grabs" calculation.

Example 3: Reddit Intelligence

{
"task": "reddit_research",
"businessName": "Joe's Pizza",
"city": "New York",
"state": "NY"
}

Returns: Business mentions on Reddit, local subreddit discussions (r/nyc, r/newyorkcity), competitor mentions, "best pizza" recommendation threads, sentiment analysis.

Example 4: LocalFalcon-Style Grid Tracking

{
"task": "serp_track",
"businessName": "Joe's Pizza",
"city": "New York",
"state": "NY",
"keywords": ["pizza near me", "best pizza"],
"gridSize": 7,
"gridRadius": 2
}

Returns: Local pack rankings, 7x7 grid (49 points) showing rank at each location, visibility score, heatmap data.

Example 5: With Webhook (Zapier/Make/n8n)

{
"task": "gbp_scrape",
"businessName": "Joe's Pizza",
"city": "New York",
"state": "NY",
"webhookUrl": "https://hooks.zapier.com/hooks/catch/12345/abcdef/"
}

Results automatically POST to your webhook when complete.

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Webhook Integration (Zapier, Make, n8n)

Webhook Payload Format

When the task completes, we POST:

{
"event": "task_completed",
"timestamp": "2024-12-18T20:00:00.000Z",
"actor": "local-business-intelligence-suite",
"runId": "abc123",
"task": "gbp_scrape",
"status": "success",
"businessName": "Joe's Pizza",
"location": "New York, NY",
"duration": 45000,
"dataPoints": 150,
"data": { ... },
"meta": {
"taskType": "gbp_scrape",
"success": true,
"estimatedCost": 1.15
}
}

Zapier Setup

1. Create a new Zap with "Webhooks by Zapier" as trigger

2. Select "Catch Hook"

3. Copy the webhook URL

4. Paste into the webhookUrl field when running the actor

5. Add actions: Send to Google Sheets, Slack, email, CRM, etc.

Make (Integromat) Setup

1. Create a new Scenario with "Webhooks" module

2. Select "Custom webhook"

3. Copy the webhook URL

4. Add to actor input

5. Build your automation flow

n8n Setup

1. Create a new Workflow with "Webhook" node

2. Set HTTP Method to POST

3. Copy the test/production URL

4. Add to actor input

5. Connect to your data destinations

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Perfect For

Local SEO Agencies

Client onboarding: Run full_audit to establish baselines in minutes, not hours
Monthly reporting: Automate serp_track and review_scrape for all clients
Competitive pitches: Show prospects their gaps vs. competitors with real data

Marketing Agencies

Lead qualification: Use tech_stack_scan to identify high-value prospects
Reputation management: Monitor reviews across all platforms with sentiment analysis
Content strategy: Use keyword_research and reddit_research for topic ideas

Franchise & Multi-Location Brands

Citation consistency: Audit NAP across all locations at once
Performance benchmarking: Compare locations against each other
Market expansion: Use market_intelligence before opening new locations

Business Consultants

Market entry analysis: Census data + competitor counts = informed recommendations
Business valuations: Review volume, sentiment, and market position data
Growth planning: 5-year projections based on County Business Patterns

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Input Parameters

Required for All Tasks

Optional Parameters

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Pay-Per-Event Pricing (18 Consolidated Events)

You only pay for what you use. No monthly fees. No minimums.

The pricing uses 18 consolidated billing events with per-unit pricing. The count parameter multiplies the unit price based on data extracted.

How Per-Unit Pricing Works

Each event charges based on the actual data extracted:

Cost Comparison

And you get 14 more modules the others don't offer.

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Output Format

All tasks return structured JSON:

{
"task": "market_intelligence",
"status": "success",
"businessName": "Joe's Pizza",
"location": "New York, NY",
"timestamp": "2024-12-18T20:00:00.000Z",
"duration": 12000,
"dataPoints": 25,
"data": {
"location": {
"city": "New York",
"state": "NY",
"zipCode": "10001",
"county": "New York County",
"stateFips": "36",
"countyFips": "061"
},
"population": {
"current": 21102,
"medianIncome": 78250,
"growthRate": 0.012,
"projected5Year": 22384
},
"competition": {
"localEstablishments": 127,
"localEmployees": 1893,
"avgEmployeesPerBusiness": 14.9,
"marketSharePotential": 823000
},
"marketSizing": {
"estimatedLocalMarket": 104521000,
"dollarsUpForGrabs5Year": 12800000
},
"projections": {
"projected5YearCompetitors": 142,
"projected5YearMarket": 117300000,
"opportunities": [...]
},
"dataSources": ["US Census Bureau", "County Business Patterns", "SBA"]
},
"billingEvents": [...]
}

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API Integration

Using the Apify API

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('localhowl/local-business-intelligence-suite').call({
task: 'market_intelligence',
businessName: "Joe's Pizza",
city: 'New York',
state: 'NY',
zipCode: '10001',
webhookUrl: 'https://your-webhook.com/endpoint'
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0].data);

Using cURL

curl -X POST "https://api.apify.com/v2/acts/localhowl~local-business-intelligence-suite/runs?token=YOUR_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"task": "full_audit",
"businessName": "Joes Pizza",
"city": "New York",
"state": "NY",
"webhookUrl": "https://hooks.zapier.com/hooks/catch/..."
}'

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MCP Server (AI Assistant Integration)

This actor functions as an MCP Server (Model Context Protocol), allowing AI assistants like Claude to directly query local business intelligence.

Available MCP Tools

Setting Up MCP Server

1. Add to Claude Desktop config (~/.config/claude/claude_desktop_config.json):

{
"mcpServers": {
"local-seo": {
"command": "npx",
"args": ["localhowl-local-seo-mcp"]
}
}
}

2. Restart Claude Desktop and start asking questions!

Example Conversations

User: "What's the Google rating for Joe's Pizza in NYC?" Claude: Uses get_business_profile tool

Joe's Pizza has a 4.7 star rating with 523 reviews. The profile is verified. They have 12 posts and 45 photos.

User: "What's the market opportunity in that ZIP code?" Claude: Uses analyze_market tool

ZIP 10001 has 21,102 residents with a median household income of $78,250. There are 127 pizza restaurants in the county employing 1,893 people. Based on 5-year population projections and market growth, there's approximately $12.8M in new revenue opportunity for market entrants.

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Changelog

v1.6.0 (December 2024)

NEW: Reputation Aggregator - Smart multi-platform review aggregation
Auto-detects business type from GBP category (100+ categories mapped)
Platforms auto-enabled by type: restaurants get Yelp/TripAdvisor/DoorDash, attorneys get Avvo, healthcare gets HealthGrades, etc.
BYOK support: D&B Direct+ (firmographics), TripAdvisor Content API, G2 API
Scrapers for: Yelp, BBB, Trustpilot, Angi, Houzz, Avvo, HealthGrades, Glassdoor, Clutch
Unified reputation score (0-100) weighted by platform importance

v1.5.0 (December 2024)

Market Intelligence: US Census Bureau, County Business Patterns, SBA data
Population demographics, competitor counts, market sizing
5-year projections and "dollars up for grabs" calculations
Webhook support: Native integration with Zapier, Make, n8n
Multiple webhook URLs supported

v1.4.0 (December 2024)

Reddit Research: Business mentions, local discussions, competitor intel
Recommendation thread discovery
Sentiment analysis for Reddit posts and comments

v1.3.0 (December 2024)

7 NEW intelligence modules: Citation Audit, Backlink Analysis, Social Presence, Keyword Research, Review Response Analysis, Photo Analysis, Hours & Attributes Audit
Technical SEO scan (robots.txt, sitemap.xml, llms.txt, security.txt)
Total tasks now: 15 specialized intelligence modules

v1.2.0 (December 2024)

Tech Stack Scan module
Detect 50+ tracking codes, analytics, pixels, CMS platforms

v1.0.0 (December 2024)

Initial release with 7 intelligence tasks
Pay-per-event pricing
Sentiment analysis for reviews
Map grid analysis for local rankings

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Limitations

Facebook: Limited to public data (no authenticated scraping)
Rate Limiting: Built-in delays to avoid blocks
Geographic Data: Uses ZIP centroid coordinates; for precise locations, provide exact coordinates
LinkedIn: Public company data only
Census Data: US only (uses Census Bureau APIs)

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Support

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Built by John Rippy | johnrippy.link