# Google Maps Lead Enrichment (`vedder1980/google-maps-lead-enrichment`) Actor Find and normalize Google Maps business leads for sales, partnerships, and local market research. - **URL**: https://apify.com/vedder1980/google-maps-lead-enrichment.md - **Developed by:** [Daniel Ferreira de Almeida](https://apify.com/vedder1980) (community) - **Categories:** Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 Enrichment A professional Apify Actor for discovering and normalizing Google Maps business leads for sales, partnerships, and local market research. ### What this Actor does This Actor searches Google Maps for commercially relevant businesses, extracts structured place data, and normalizes the output into a clean lead dataset. ### Best-fit use cases - outbound lead generation - local market research - partnership discovery - competitive mapping - CRM enrichment ### Input - search terms - location - optional maximum result cap per term - optional website and rating filters - optional detailed place scraping ### Output Each result is normalized with: - business name - category - address and location fields - phone and website - website domain - rating and review count - maps URL - coordinates - opening hours when available - social/contact enrichment when available ### Operating principles - keep the output deterministic - keep the input contract narrow - favor buyer value over broad scraping flexibility - keep compute usage low enough for the free Apify plan - prefer a single city and a single commercial search term for initial validation ### Recommended first run Start with one city and one high-intent search term. Example: - search term: `dentist` - location: `Lisbon, Portugal` ### Quality bar This Actor should only be published after: - real data validation - stable output shape - understandable error messages - acceptable cost per usable lead ### Notes This Actor is designed as a productized lead-generation asset, not a generic scraper. # Actor input Schema ## `searchStringsArray` (type: `array`): Search terms to query in Google Maps. Use precise, commercially relevant terms. ## `locationQuery` (type: `string`): City, region, or country to scope the search. ## `maxCrawledPlacesPerSearch` (type: `integer`): Upper bound for each search term. ## `language` (type: `string`): Language for the resulting place details. ## `placeMinimumStars` (type: `string`): Optional minimum star rating filter. ## `skipClosedPlaces` (type: `boolean`): Exclude temporarily and permanently closed places. ## `scrapePlaceDetailPage` (type: `boolean`): Collect richer fields such as reviews distribution and opening hours. ## `website` (type: `string`): Filter places by website availability. ## `includeRawPlaces` (type: `boolean`): Store the original scraped place records alongside the normalized output. ## Actor input object example ```json { "searchStringsArray": [ "restaurant" ], "locationQuery": "Lisbon, Portugal", "maxCrawledPlacesPerSearch": 10, "language": "en", "placeMinimumStars": "", "skipClosedPlaces": true, "scrapePlaceDetailPage": true, "website": "withWebsite", "includeRawPlaces": false } ```` # Actor output Schema ## `results` (type: `string`): Default dataset with one normalized lead per item. # 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("vedder1980/google-maps-lead-enrichment").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("vedder1980/google-maps-lead-enrichment").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 vedder1980/google-maps-lead-enrichment --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=vedder1980/google-maps-lead-enrichment", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Maps Lead Enrichment", "description": "Find and normalize Google Maps business leads for sales, partnerships, and local market research.", "version": "1.0", "x-build-id": "UfJgff7NMmZQ6Cjot" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/vedder1980~google-maps-lead-enrichment/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-vedder1980-google-maps-lead-enrichment", "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/vedder1980~google-maps-lead-enrichment/runs": { "post": { "operationId": "runs-sync-vedder1980-google-maps-lead-enrichment", "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/vedder1980~google-maps-lead-enrichment/run-sync": { "post": { "operationId": "run-sync-vedder1980-google-maps-lead-enrichment", "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", "required": [ "searchStringsArray", "locationQuery" ], "properties": { "searchStringsArray": { "title": "Search terms", "type": "array", "description": "Search terms to query in Google Maps. Use precise, commercially relevant terms.", "items": { "type": "string" }, "default": [ "restaurant" ] }, "locationQuery": { "title": "Location", "type": "string", "description": "City, region, or country to scope the search.", "default": "Lisbon, Portugal" }, "maxCrawledPlacesPerSearch": { "title": "Maximum places per search term", "minimum": 1, "type": "integer", "description": "Upper bound for each search term.", "default": 10 }, "language": { "title": "Language", "type": "string", "description": "Language for the resulting place details.", "default": "en" }, "placeMinimumStars": { "title": "Minimum rating", "type": "string", "description": "Optional minimum star rating filter.", "default": "" }, "skipClosedPlaces": { "title": "Skip closed places", "type": "boolean", "description": "Exclude temporarily and permanently closed places.", "default": true }, "scrapePlaceDetailPage": { "title": "Scrape place detail pages", "type": "boolean", "description": "Collect richer fields such as reviews distribution and opening hours.", "default": true }, "website": { "title": "Website filter", "enum": [ "allPlaces", "withWebsite", "withoutWebsite" ], "type": "string", "description": "Filter places by website availability.", "default": "withWebsite" }, "includeRawPlaces": { "title": "Include raw source records", "type": "boolean", "description": "Store the original scraped place records alongside the normalized output.", "default": false } } }, "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 } } } } } } } } } } ```