# Buyer Fit Brief API - ICP Scoring for Lead Datasets (`webdata_labs/buyer-fit-brief-api`) Actor \[💵 $2.00 / 1K] Score Apify lead datasets against your ICP and return ranked buyer-fit briefs with matched signals, disqualification flags, pain points, and outreach angles. - **URL**: https://apify.com/webdata\_labs/buyer-fit-brief-api.md - **Developed by:** [WebData Labs](https://apify.com/webdata_labs) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 buyer-fit lead rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## Buyer Fit Brief API - ICP Scoring for Lead Datasets Score Apify lead datasets against your ideal customer profile and return ranked buyer-fit briefs, not a black-box "AI lead score." This Actor consumes inline JSON records or an Apify dataset ID and emits one buyer-fit row per company or lead. Each row includes a 0-100 score, fit tier, confidence, matched signals, disqualification flags, likely pain points, outreach angle, and suggested next step. ### ✅ What you get / ❌ what this isn't | This Actor gives you | This Actor is not | |---|---| | ICP fit scores with auditable reasons | Not a scraper | | Ranked lead rows for CRM prioritization | Not a generic sales agent | | Matched signals and disqualification flags | Not an opaque LLM-only score | | Outreach angles grounded in the input data | Not a replacement for human qualification | ### ⚙️ How to score lead datasets - Prioritize scraped lead lists before CRM import. - Score output from Lead List Deduplicator, Website Contact Extractor, Google Maps lead scrapers, directories, event lists, or CSV exports. - Keep scoring cheap and predictable with deterministic rules in v1. - See why a lead scored high or low before spending enrichment or outreach credits. ### 📥 Input ```json { "datasetId": "optional-apify-dataset-id", "icpDescription": "B2B SaaS or logistics companies in the United States with manual data workflows.", "productDescription": "A data automation API that turns messy public data into workflow-ready records.", "positiveKeywords": ["b2b", "saas", "logistics", "automation"], "negativeKeywords": ["student", "consumer blog", "hobby"], "targetIndustries": ["software", "logistics", "ecommerce"], "targetCountries": ["United States", "Canada"], "minEmployeeCount": 10, "maxEmployeeCount": 1000 } ```` You can also paste `records` directly for quick tests. ### 📤 Output ```json { "recordType": "buyerFitBrief", "sourceRowId": "sample-1", "companyName": "Northstar Logistics", "domain": "northstarlogistics.example", "industry": "freight and logistics", "country": "United States", "employeeCount": 85, "fitScore": 92, "fitTier": "high", "confidence": 0.95, "matchedSignals": ["has_domain", "positive_keyword:logistics"], "disqualificationFlags": [], "fitRationale": "HIGH fit (92/100) based on has_company_name, has_domain...", "likelyPainPoints": ["manual workflow cleanup"], "outreachAngle": "Lead with manual workflow cleanup at Northstar Logistics...", "suggestedNextStep": "Prioritize for personalized outreach or enrichment before CRM import." } ``` ### 📊 Dataset views | View | Best for | |---|---| | Ranked leads | Prioritized CRM or outreach list | | Buyer-fit briefs | Rationale, pain points, and suggested next step | | Low-fit or disqualified | Rows to hold out or review manually | ### 💵 How much does it cost? This Actor is priced per scored lead row. The default listing price is $2.00 per 1,000 scored rows, with lower per-row pricing for higher Apify usage tiers. ### ⚠️ Limits and caveats - v1 uses deterministic scoring, not paid LLM adjudication. - Scores depend on the fields present in your input. Sparse records get lower confidence. - The Actor does not scrape, enrich missing websites, or verify emails. - Treat the output as a prioritization layer, not a compliance or credit decision. ### 🛠️ Related workflow 1. Scrape leads. 2. Clean them with Lead List Deduplicator. 3. Score them here against your ICP. 4. Enrich or contact the highest-fit rows first. # Actor input Schema ## `records` (type: `array`): Optional inline company or lead records. Use this for quick tests or small lists. ## `datasetId` (type: `string`): Optional Apify dataset ID to read records from. If provided, records are loaded from this dataset. ## `icpDescription` (type: `string`): Describe the buyer you want: industry, company size, pain, region, and buying trigger. ## `productDescription` (type: `string`): Describe what you sell so the Actor can draft a relevant outreach angle. ## `positiveKeywords` (type: `array`): Terms that should increase fit when found in company fields. ## `negativeKeywords` (type: `array`): Terms that should reduce fit or disqualify leads. ## `targetIndustries` (type: `array`): Optional industry terms to match more strongly than generic positive keywords. ## `targetCountries` (type: `array`): Optional country or region terms to prefer. ## `minEmployeeCount` (type: `integer`): Optional lower bound for company size. Set to 0 to ignore. ## `maxEmployeeCount` (type: `integer`): Optional upper bound for company size. Set to 0 to ignore. ## `maxRecords` (type: `integer`): Maximum records to process from inline input or dataset. ## `includeOriginalRecord` (type: `boolean`): Include the source record in each output row for audit/debugging. ## Actor input object example ```json { "records": [ { "id": "sample-1", "company": "Northstar Logistics", "domain": "northstarlogistics.example", "industry": "freight and logistics", "description": "Regional freight broker using manual invoice and document workflows for shippers.", "country": "United States", "employeeCount": 85 }, { "id": "sample-2", "company": "BrightCart", "website": "https://brightcart.example", "industry": "ecommerce software", "description": "B2B SaaS platform helping Shopify brands monitor product feeds and pricing.", "country": "United States", "employeeCount": 42 }, { "id": "sample-3", "company": "City Food Blog", "website": "https://cityfoodblog.example", "industry": "media", "description": "Consumer food blog and newsletter.", "country": "United Kingdom", "employeeCount": 3 } ], "icpDescription": "B2B SaaS or logistics companies in the United States with manual data, document, review, or lead workflows. Prefer teams with 20-500 employees and clear operations or revenue pain.", "productDescription": "A data/API automation service that turns messy public or scraped data into reliable, workflow-ready records.", "positiveKeywords": [ "b2b", "saas", "logistics", "freight", "operations", "data", "api", "automation", "crm", "ecommerce", "shopify", "reviews", "procurement" ], "negativeKeywords": [ "student", "job seeker", "consumer blog", "news blog", "blog", "hobby", "personal site", "nonprofit" ], "targetIndustries": [ "software", "saas", "logistics", "freight", "ecommerce", "marketplace", "agency", "real estate", "procurement" ], "targetCountries": [ "United States", "US", "USA", "Canada", "United Kingdom", "UK" ], "minEmployeeCount": 10, "maxEmployeeCount": 1000, "maxRecords": 5000, "includeOriginalRecord": false } ``` # Actor output Schema ## `rankedLeads` (type: `string`): No description ## `briefs` (type: `string`): No description ## `disqualified` (type: `string`): No description ## `output` (type: `string`): No description # 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("webdata_labs/buyer-fit-brief-api").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("webdata_labs/buyer-fit-brief-api").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 webdata_labs/buyer-fit-brief-api --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=webdata_labs/buyer-fit-brief-api", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Buyer Fit Brief API - ICP Scoring for Lead Datasets", "description": "[💵 $2.00 / 1K] Score Apify lead datasets against your ICP and return ranked buyer-fit briefs with matched signals, disqualification flags, pain points, and outreach angles.", "version": "0.1", "x-build-id": "MXoe2iRHWam0aPfAd" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/webdata_labs~buyer-fit-brief-api/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-webdata_labs-buyer-fit-brief-api", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/webdata_labs~buyer-fit-brief-api/runs": { "post": { "operationId": "runs-sync-webdata_labs-buyer-fit-brief-api", "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/webdata_labs~buyer-fit-brief-api/run-sync": { "post": { "operationId": "run-sync-webdata_labs-buyer-fit-brief-api", "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": { "records": { "title": "Inline lead records", "type": "array", "description": "Optional inline company or lead records. Use this for quick tests or small lists.", "items": { "type": "object" }, "default": [ { "id": "sample-1", "company": "Northstar Logistics", "domain": "northstarlogistics.example", "industry": "freight and logistics", "description": "Regional freight broker using manual invoice and document workflows for shippers.", "country": "United States", "employeeCount": 85 }, { "id": "sample-2", "company": "BrightCart", "website": "https://brightcart.example", "industry": "ecommerce software", "description": "B2B SaaS platform helping Shopify brands monitor product feeds and pricing.", "country": "United States", "employeeCount": 42 }, { "id": "sample-3", "company": "City Food Blog", "website": "https://cityfoodblog.example", "industry": "media", "description": "Consumer food blog and newsletter.", "country": "United Kingdom", "employeeCount": 3 } ] }, "datasetId": { "title": "Apify dataset ID", "type": "string", "description": "Optional Apify dataset ID to read records from. If provided, records are loaded from this dataset." }, "icpDescription": { "title": "Ideal customer profile", "type": "string", "description": "Describe the buyer you want: industry, company size, pain, region, and buying trigger.", "default": "B2B SaaS or logistics companies in the United States with manual data, document, review, or lead workflows. Prefer teams with 20-500 employees and clear operations or revenue pain." }, "productDescription": { "title": "Product or offer", "type": "string", "description": "Describe what you sell so the Actor can draft a relevant outreach angle.", "default": "A data/API automation service that turns messy public or scraped data into reliable, workflow-ready records." }, "positiveKeywords": { "title": "Positive fit keywords", "type": "array", "description": "Terms that should increase fit when found in company fields.", "items": { "type": "string" }, "default": [ "b2b", "saas", "logistics", "freight", "operations", "data", "api", "automation", "crm", "ecommerce", "shopify", "reviews", "procurement" ] }, "negativeKeywords": { "title": "Negative fit keywords", "type": "array", "description": "Terms that should reduce fit or disqualify leads.", "items": { "type": "string" }, "default": [ "student", "job seeker", "consumer blog", "news blog", "blog", "hobby", "personal site", "nonprofit" ] }, "targetIndustries": { "title": "Target industries", "type": "array", "description": "Optional industry terms to match more strongly than generic positive keywords.", "items": { "type": "string" }, "default": [ "software", "saas", "logistics", "freight", "ecommerce", "marketplace", "agency", "real estate", "procurement" ] }, "targetCountries": { "title": "Target countries", "type": "array", "description": "Optional country or region terms to prefer.", "items": { "type": "string" }, "default": [ "United States", "US", "USA", "Canada", "United Kingdom", "UK" ] }, "minEmployeeCount": { "title": "Minimum employees", "minimum": 0, "type": "integer", "description": "Optional lower bound for company size. Set to 0 to ignore.", "default": 10 }, "maxEmployeeCount": { "title": "Maximum employees", "minimum": 0, "type": "integer", "description": "Optional upper bound for company size. Set to 0 to ignore.", "default": 1000 }, "maxRecords": { "title": "Maximum records", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum records to process from inline input or dataset.", "default": 5000 }, "includeOriginalRecord": { "title": "Include original record", "type": "boolean", "description": "Include the source record in each output row for audit/debugging.", "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 } } } } } } } } } } ```