# Google Maps Lead Enricher (`prabhat007/google-maps-lead-enricher`) Actor Take Google Maps business listings and enrich each with emails, decision-maker names, social profiles, and tech stack — ready for HubSpot, Salesforce, or n8n. - **URL**: https://apify.com/prabhat007/google-maps-lead-enricher.md - **Developed by:** [prabhat pankaj](https://apify.com/prabhat007) (community) - **Categories:** Lead generation, E-commerce, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Google Maps Lead Enricher Turn raw Google Maps listings into **sales-ready leads** with emails, decision-maker names, social profiles, and tech stack. Drops straight into HubSpot, Salesforce, Pipedrive, or n8n / Zapier / Make. ### What it does 1. Takes business listings as input — either a Google Maps search query, a previous `compass/crawler-google-places` run ID, or a JSON array you supply. 2. Visits each business website (homepage + likely `/contact`, `/about`, `/team` pages). 3. Extracts: - Business emails (deduped, domain-prioritised) - Phone numbers - Social profile URLs (LinkedIn, Twitter/X, Facebook, Instagram, YouTube, TikTok) - Likely decision makers — owner / founder / CEO / partner names - Tech stack — Shopify, WordPress, HubSpot, Intercom, Stripe, GA4, Meta Pixel, etc. 4. Outputs a clean dataset, one row per lead. ### Inputs | Field | Type | Notes | |---|---|---| | `searchQuery` | string | e.g. `dentists in Austin, TX` — used if you don't supply businesses | | `businesses` | array | Skip the Maps step — pass your own `[ { name, website, ... } ]` | | `googleMapsScraperRunId` | string | Reuse an existing `compass/crawler-google-places` run | | `maxLeads` | int | Hard cap on leads (and cost) | | `findDecisionMakers` | bool | Extract owner/founder/CEO names | | `detectTechStack` | bool | Detect 18+ common tools | | `verifyEmails` | bool | DNS MX lookup per email; marks `mxValid`, `isFreeProvider`, `isRoleAddress` | | `enrichLinkedIn` | bool | Call a LinkedIn company scraper — adds industry, HQ, headcount, follower count, founded year | | `linkedinActorId` | string | Apify actor for LinkedIn company (default `dev_fusion/linkedin-company-scraper`) | | `enrichLinkedInEmployees` | bool | Call employee-search actor and merge LinkedIn decision-makers (CEO, VP, head of...) | | `linkedinEmployeeActorId` | string | Apify actor for LinkedIn employees (default `harvestapi/linkedin-employees`) | | `maxEmployeesPerCompany` | int | Cap employee search per company (default 25) | | `pushToHubSpot` | bool | After enrichment, upsert contacts into HubSpot CRM | | `hubspotApiToken` | string (secret) | HubSpot private app token — required if `pushToHubSpot=true` | | `proxyConfiguration` | object | Apify Proxy — residential recommended at scale | ### Pricing (Pay-Per-Event) | Event | Price | When | |---|---|---| | `lead-enriched` | $0.01 | Per lead successfully visited | | `email-found` | $0.03 | When at least one email is extracted | | `decision-maker-found` | $0.05 | When an owner/founder/CEO is identified | | `verified-email-found` | $0.05 | Per email passing DNS MX validation | | `linkedin-enriched` | $0.10 | Per successful LinkedIn company profile fetch | | `linkedin-employee-found` | $0.20 | Per LinkedIn decision-maker added (CEO, VP, founder, etc.) | | `crm-pushed` | $0.02 | Per HubSpot contact created/updated | Typical batch of 100 leads with ~40% email hit-rate and ~15% decision-maker hit-rate ≈ **$2.95** (LinkedIn off). With LinkedIn enrichment at ~30% hit rate → add ~$3, total ≈ **$6** per 100 leads. ### Output shape ```json { "name": "Blue Bottle Coffee", "website": "https://bluebottlecoffee.com", "address": "76 N 4th St, Brooklyn, NY", "phone": "+1 510-653-3394", "rating": 4.5, "emails": ["hello@bluebottlecoffee.com"], "phones": ["+1 510-653-3394"], "socials": { "linkedin": "https://linkedin.com/company/blue-bottle-coffee", "instagram": "https://instagram.com/bluebottle" }, "decisionMakers": [ { "name": "Bryan Meehan", "role": "ceo", "source": "https://bluebottlecoffee.com/about" } ], "techStack": ["Shopify", "Klaviyo", "Meta Pixel", "GA4"], "contactPageUrl": "https://bluebottlecoffee.com/contact", "enrichedAt": "2026-06-09T12:00:00.000Z" } ```` ### Run locally ```bash npm install npx apify run ``` Edit `.actor/input.json` to change the search query. ### Roadmap - \[x] LinkedIn company enrichment (industry, headcount, HQ, follower count) - \[x] LinkedIn employee enrichment → decision-makers with real names + titles - \[x] MX email validation - \[x] HubSpot CRM push - \[ ] Email pattern guesser (firstname@domain, f.lastname@domain) - \[ ] Salesforce push - \[ ] Funding / hiring signals (jobs page detection) # Actor input Schema ## `searchQuery` (type: `string`): What to search on Google Maps (e.g. 'dentists in Austin, TX'). Used when no businesses or googleMapsScraperRunId is provided. ## `businesses` (type: `array`): Pre-collected business records. Each should have at least { name, website } or { name, address }. ## `googleMapsScraperRunId` (type: `string`): Run ID from apify/google-maps-scraper — we read its dataset instead of scraping fresh. ## `maxLeads` (type: `integer`): Hard cap on enriched leads (controls cost). ## `findDecisionMakers` (type: `boolean`): Visit /about, /team, LinkedIn — extract likely owner / founder / CEO names. Triggers a paid event per lead enriched. ## `detectTechStack` (type: `boolean`): Detect Shopify / WordPress / HubSpot / Intercom / GA / Meta Pixel from the homepage. ## `verifyEmails` (type: `boolean`): DNS MX lookup per email domain. Marks each email mxValid / isFreeProvider / isRoleAddress. Verified emails trigger the `verified-email-found` paid event. ## `enrichLinkedIn` (type: `boolean`): For leads where we found a LinkedIn company URL, call an external LinkedIn scraper to add industry, HQ, headcount, follower count, founded year. Triggers the `linkedin-enriched` paid event. ## `linkedinActorId` (type: `string`): Apify Store actor used for LinkedIn enrichment. Default: `dev_fusion/linkedin-company-scraper`. Override if you prefer harvestapi/, apimaestro/, or another LinkedIn actor. ## `enrichLinkedInEmployees` (type: `boolean`): For each company with a LinkedIn URL, call an employee-search actor and merge anyone with a decision-maker title (CEO, founder, VP, director, head of...) into decisionMakers. Triggers the `linkedin-employee-found` paid event. ## `linkedinEmployeeActorId` (type: `string`): Apify actor used for LinkedIn employee search. Default: `harvestapi/linkedin-employees`. ## `maxEmployeesPerCompany` (type: `integer`): Hard cap on LinkedIn employees fetched per company (controls cost). ## `linkedinTimeoutSecs` (type: `integer`): Max wait per LinkedIn enrichment call before giving up. ## `pushToHubSpot` (type: `boolean`): After enrichment, upsert each lead's contacts into HubSpot via the CRM Contacts API. Triggers the `crm-pushed` paid event per contact created/updated. ## `hubspotApiToken` (type: `string`): HubSpot private app access token (Bearer). Required if pushToHubSpot=true. Stored encrypted by Apify. ## `proxyConfiguration` (type: `object`): Apify Proxy settings. Residential recommended for large runs. ## Actor input object example ```json { "searchQuery": "coffee shops in Brooklyn, NY", "businesses": [], "maxLeads": 100, "findDecisionMakers": true, "detectTechStack": true, "verifyEmails": true, "enrichLinkedIn": false, "linkedinActorId": "dev_fusion/linkedin-company-scraper", "enrichLinkedInEmployees": false, "linkedinEmployeeActorId": "harvestapi/linkedin-employees", "maxEmployeesPerCompany": 25, "linkedinTimeoutSecs": 120, "pushToHubSpot": false, "proxyConfiguration": { "useApifyProxy": true } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("prabhat007/google-maps-lead-enricher").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("prabhat007/google-maps-lead-enricher").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call prabhat007/google-maps-lead-enricher --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=prabhat007/google-maps-lead-enricher", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Maps Lead Enricher", "description": "Take Google Maps business listings and enrich each with emails, decision-maker names, social profiles, and tech stack — ready for HubSpot, Salesforce, or n8n.", "version": "0.2", "x-build-id": "qD3MsDOB7TrA3QpP7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/prabhat007~google-maps-lead-enricher/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-prabhat007-google-maps-lead-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/prabhat007~google-maps-lead-enricher/runs": { "post": { "operationId": "runs-sync-prabhat007-google-maps-lead-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/prabhat007~google-maps-lead-enricher/run-sync": { "post": { "operationId": "run-sync-prabhat007-google-maps-lead-enricher", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "searchQuery": { "title": "Search query", "type": "string", "description": "What to search on Google Maps (e.g. 'dentists in Austin, TX'). Used when no businesses or googleMapsScraperRunId is provided." }, "businesses": { "title": "Businesses (manual input)", "type": "array", "description": "Pre-collected business records. Each should have at least { name, website } or { name, address }.", "default": [] }, "googleMapsScraperRunId": { "title": "Reuse Google Maps Scraper run", "type": "string", "description": "Run ID from apify/google-maps-scraper — we read its dataset instead of scraping fresh." }, "maxLeads": { "title": "Max leads to enrich", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Hard cap on enriched leads (controls cost).", "default": 100 }, "findDecisionMakers": { "title": "Find decision-makers", "type": "boolean", "description": "Visit /about, /team, LinkedIn — extract likely owner / founder / CEO names. Triggers a paid event per lead enriched.", "default": true }, "detectTechStack": { "title": "Detect tech stack", "type": "boolean", "description": "Detect Shopify / WordPress / HubSpot / Intercom / GA / Meta Pixel from the homepage.", "default": true }, "verifyEmails": { "title": "Verify emails (MX lookup)", "type": "boolean", "description": "DNS MX lookup per email domain. Marks each email mxValid / isFreeProvider / isRoleAddress. Verified emails trigger the `verified-email-found` paid event.", "default": true }, "enrichLinkedIn": { "title": "Enrich with LinkedIn (paid)", "type": "boolean", "description": "For leads where we found a LinkedIn company URL, call an external LinkedIn scraper to add industry, HQ, headcount, follower count, founded year. Triggers the `linkedin-enriched` paid event.", "default": false }, "linkedinActorId": { "title": "LinkedIn actor ID", "type": "string", "description": "Apify Store actor used for LinkedIn enrichment. Default: `dev_fusion/linkedin-company-scraper`. Override if you prefer harvestapi/, apimaestro/, or another LinkedIn actor.", "default": "dev_fusion/linkedin-company-scraper" }, "enrichLinkedInEmployees": { "title": "Enrich LinkedIn employees (decision-makers)", "type": "boolean", "description": "For each company with a LinkedIn URL, call an employee-search actor and merge anyone with a decision-maker title (CEO, founder, VP, director, head of...) into decisionMakers. Triggers the `linkedin-employee-found` paid event.", "default": false }, "linkedinEmployeeActorId": { "title": "LinkedIn employee actor ID", "type": "string", "description": "Apify actor used for LinkedIn employee search. Default: `harvestapi/linkedin-employees`.", "default": "harvestapi/linkedin-employees" }, "maxEmployeesPerCompany": { "title": "Max employees per company", "minimum": 1, "maximum": 200, "type": "integer", "description": "Hard cap on LinkedIn employees fetched per company (controls cost).", "default": 25 }, "linkedinTimeoutSecs": { "title": "LinkedIn call timeout (seconds)", "minimum": 30, "maximum": 600, "type": "integer", "description": "Max wait per LinkedIn enrichment call before giving up.", "default": 120 }, "pushToHubSpot": { "title": "Push to HubSpot CRM", "type": "boolean", "description": "After enrichment, upsert each lead's contacts into HubSpot via the CRM Contacts API. Triggers the `crm-pushed` paid event per contact created/updated.", "default": false }, "hubspotApiToken": { "title": "HubSpot private app token", "type": "string", "description": "HubSpot private app access token (Bearer). Required if pushToHubSpot=true. Stored encrypted by Apify." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify Proxy settings. Residential recommended for large runs.", "default": { "useApifyProxy": true } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```