Google Maps Leads Qualifier

Qualify Google Maps leads against your ideal customer profile BEFORE you pay to enrich them.

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Stop paying to enrich leads you will never email.

Typical Google Maps scrapes are 50% to 80% off-target for a specific B2B Ideal Customer Profile (franchises, public clinics, closed businesses, wrong industry).

Net Savings Formula:
Savings per 1,000 leads ≈ (1 - pass rate) × (downstream cost per lead) - $3.00

• At a typical 37% pass rate and $0.036 downstream enrichment cost:
  Save ≈ $19.68 net per 1,000 leads, while concentrating your mailboxes on 2.7× more on-target decision makers.
• Enter your actual downstream costs in the downstreamCostPerLead input field and each run will automatically report your exact dollar ROI in the SUMMARY view.

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⚡ Auto-Qualify Every Google Maps Scrape (3-Click Integration)

Attach this Actor as an automatic filter directly downstream of any Google Maps scraper (e.g. compass/crawler-google-places):

1. In your Google Maps Scraper on Apify, click the Integrations tab.
2. Click Add integration → Run an Actor, and select norvane/google-maps-leads-qualifier.
3. In the input configuration, enter:

{
  "datasetId": "{{resource.defaultDatasetId}}",
  "icpDescription": "Your ideal customer profile and exclusions..."
}

Whenever your scraper completes, your leads are automatically filtered and scored before you touch or export them.

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What You Get Per Lead

Every processed row retains all original Google Maps fields and appends our frozen Version 1 qualification fields:

Field	Type	Description	Example
qualified	boolean	true if probability >= threshold and business is not closed	true
icp_probability	float (0.00 - 1.00)	Calibrated probability of match against your ICP	0.94
fit_score	float (1.0 - 10.0)	Calibrated qualitative fit rating	9.4
business_type	string	Categorization: independent, chain, franchise, government, nonprofit, unclear	independent
customer_model	string	Focus: B2B, B2C, both, unclear	B2B
website_status	string	Live analysis status: live, none, social_only, platform_page, dead, blocked	live
confidence_band	string	Decision reliability band: high, medium, uncertain	high
reason_code	string	Deterministic explanation code: site_matched, category_only, closed, etc.	site_matched
schemaVersion	integer	Guaranteed immutable schema version (never breaking your pipelines)	1

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3-Step Quickstart

1. Scrape Google Maps using any scraper (e.g. compass/crawler-google-places) WITHOUT paid enrichment add-ons.
2. Open Google Maps Leads Qualifier, paste the datasetId (or pass it via an Apify Integration Task).
3. Describe your Ideal Customer (ICP) in plain English (e.g. "Independent dental clinics offering cosmetic dentistry or implants; exclude hospitals and corporate dental chains"), select your threshold, and click Save & Run.

💡 Try it free: Leave datasetId empty to run an immediate, zero-cost demo on our pre-loaded 20-row sample dataset.

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Transparent Pay-Per-Event (PPE) Pricing

Event	Price	When Billed
Lead Qualified (with Website Analysis)	$0.003 per lead ($3.00 / 1k)	Business evaluated with live homepage content fetch
Lead Qualified (Metadata Only)	$0.001 per lead ($1.00 / 1k)	Evaluated from name, category & description (no website, social-only, or analyzeWebsite=false)
Duplicate Places	$0.000 (Free)	Repeated placeIds in the same dataset are deduplicated at zero charge
Demo Execution	$0.000 (Free)	Zero-input sample runs are always 100% free

Fairness Guarantee: You are charged per processed lead. Non-qualified leads are accessible in the Rejected tab view so you can audit every single decision.

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Writing an Effective ICP Description

❌ Poor / Vague ICP (High False Positives)	✅ High-Precision ICP (Recommended)
"Small businesses in healthcare"	"Independent physiotherapy and sports injury clinics with private practitioners. Exclude public health centers and hospital departments."
"Restaurants and food companies"	"High-end independent Italian restaurants offering wine pairings. Exclude fast food, pizza slices, chains, and franchise brands."
"Software companies"	"B2B SaaS companies selling human resources or payroll software. Exclude agencies, dev shops, and IT consultancies."

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Choosing Your Threshold (minProbability)

• Balanced (0.50 - Default): Ideal for standard outbound campaigns. Optimizes net dollar savings while filtering out ~60% of unqualified noise.
• High Recall (0.30): Ideal when your contract value (ACV) is high ($1,000+) and missing a valid lead is expensive. Catches almost every borderline candidate.
• High Precision (0.70): Ideal when mailbox sender capacity is strictly limited and you want only the absolute highest-conviction prospects.

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Automated Workflow Integrations

Native Apify Task Automation

Create an Apify Schedule for your Google Maps Scraper, then add an Integration → Run Actor targeting google-maps-leads-qualifier passing datasetId: "{{resource.defaultDatasetId}}". Every week, your scraped leads are automatically qualified and waiting in your dataset.

n8n Workflow Integration

Paste the following HTTP Request node into your n8n canvas:

{
  "name": "Qualify Google Maps Leads",
  "type": "n8n-nodes-base.httpRequest",
  "parameters": {
    "method": "POST",
    "url": "https://api.apify.com/v2/acts/norvane~google-maps-leads-qualifier/run-sync-get-dataset-items",
    "sendQuery": true,
    "queryParameters": {
      "parameters": [
        { "name": "token", "value": "={{$env.APIFY_TOKEN}}" }
      ]
    },
    "sendBody": true,
    "specifyBody": "json",
    "jsonBody": "={ \"datasetId\": \"{{$json.defaultDatasetId}}\", \"icpDescription\": \"Independent dental clinics offering implants\", \"minProbability\": \"0.5\" }",
    "options": {
      "timeout": 300000
    }
  }
}

Make.com Blueprint

Add the Apify: Run an Actor and Get Dataset Items module. Connect it after your Google Maps scraper and route qualified = true leads directly to your CRM (HubSpot, Pipedrive) or outreach tools (Smartlead, Instantly).

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Data Privacy & Sub-Processors

Text snippets and business metadata are transmitted over encrypted TLS to our low-latency System 1 decision engine (TypeSafe AI) solely to compute qualification probabilities and fit scores. Data is never used for training models and is processed in accordance with enterprise data protection standards.