# Lead Enrichment & Scoring - Email/Domain to ICP Fit (`renzomacar/lead-enrichment`) Actor - **URL**: https://apify.com/renzomacar/lead-enrichment.md - **Developed by:** [Renzo Madueno](https://apify.com/renzomacar) (community) - **Categories:** Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## Lead Enrichment & Scoring — Email or Domain to ICP-Fit Score Turn a raw list of **leads** — emails, domains, or names + companies — into **enriched, scored rows** ready to prioritize and route. Each lead gets firmographics, tech stack, a contact, **and a 0–100 lead score with a Hot/Warm/Cold tier and the reasons behind it** — read **live from the company's own website**, not a stale database. Unlike database tools (Apollo, Clay, ZoomInfo) that serve **cached records**, this Actor enriches **in real time** and **scores each lead against your ICP** (target industries + minimum size), so your reps work the hottest leads first. Every row is confidence-scored. ⭐ **Using it and it works? [Leave a 30-second review](https://apify.com/renzomacar/lead-enrichment/reviews)** — and tell me what signal to add next via the **Issues** tab. ### What you get One scored, CRM-ready row per lead: | Column | Example | |---|---| | `leadScore` + `leadTier` | 82 · **Hot** | | `fitReasons` | ["industry known", "decision-maker found", "matches target industry"] | | `companyName`, `domain`, `industry` | "Stripe" · stripe.com · "Financial Services & Fintech" | | `employeeBand`, `revenueBand`, `country` | "1001-5000" · "$100M-$500M" · "United States" | | `contactName`, `contactTitle`, `contactEmail` | "Jane Smith" · "VP Sales" · jane@… | | `roleEmail` | "sales@company.com" | | `techStack` | "HubSpot, Shopify, Google Analytics" | | `domainHasMx`, `mxProvider` | true · "Google Workspace" | | `confidenceScore` | 0–1 | ### How the lead score works The score rewards **firmographic completeness + reachability + ICP fit**: - **Data signals** — industry, company size, tech stack, valid mail server. - **Reachability** — a role email, a decision-maker, a direct contact email, or a known email pattern. - **ICP fit** — set `targetIndustries` and/or `minEmployeeBand` and leads that match are boosted; leads that don't are penalized. `leadTier`: **Hot** (≥70), **Warm** (40–69), **Cold** (<40). Every score ships with `fitReasons` so it's never a black box. ### How do I score my leads? 1. Paste your **leads** — one per line. Any format works: `jane@acme.com`, `acme.com`, or `Jane Smith, acme.com`. 2. (Optional) Set your **ICP**: `targetIndustries` (e.g. "SaaS", "Fintech") and `minEmployeeBand`. 3. Run it — one scored row per lead, sorted-ready for your CRM, CSV, or sequencer. ```json { "leads": ["patrick@stripe.com", "hubspot.com", "Jane Smith, acme.com"], "targetIndustries": ["SaaS", "Fintech"], "minEmployeeBand": "51-200" } ```` ### Output example ```json { "input": "hubspot.com", "companyName": "HubSpot", "industry": "Software & SaaS", "employeeBand": "5000+", "revenueBand": "$500M+", "contactName": "Yamini Rangan", "contactTitle": "Chief Executive Officer", "roleEmail": "sales@hubspot.com", "techStack": "HubSpot, Cloudflare, Google Analytics", "domainHasMx": true, "leadScore": 78, "leadTier": "Hot", "fitReasons": ["industry known", "size known", "role email found", "matches target industry"], "confidenceScore": 0.65 } ``` ### Use cases - **Lead scoring & prioritization** — work Hot leads first; skip Cold. - **Inbound routing** — route by tier, industry, and size to the right rep. - **List cleaning & ICP filtering** — score a raw list and keep only ICP-fit leads. - **CRM enrichment** — append firmographics + contact + score to your records. - **Sequencer prep** — enrich + score before pushing to your outreach tool. ### Pricing Pay-per-result — no subscription, no seat license: | Event | Price | |---|---| | Actor start | $0.01 | | Enriched & scored lead (above your confidence threshold) | $0.02 | **You only pay for substantive leads above `minConfidence`.** 1,000 leads that resolve 700 substantive rows costs about **$0.01 + 700 × $0.02 ≈ $14** — versus a per-seat sales-intelligence license. ### Lead Enrichment vs the alternatives | | This Actor | Apollo / Clay / ZoomInfo | Generic scraper | |---|---|---|---| | Live crawl (not stale DB) | ✅ | ❌ cached | ✅ | | Lead score + tier + reasons | ✅ | partial | ❌ | | ICP fit (industry + size) | ✅ | ✅ | ❌ | | Firmographics + tech stack | ✅ | ✅ | ❌ | | Confidence score per row | ✅ | ❌ | ❌ | | No API key / no seat · pay per result | ✅ | ❌ | varies | ### FAQ **How is the score calculated?** From data completeness, reachability, and ICP match — every score includes `fitReasons`. It's a transparent routing signal, not a black-box grade. **Do I need an API key?** No. It runs on Apify and exports anywhere. **What if I only have emails (or only domains)?** All work. Emails and names add a contact; domains alone still score on firmographics + reachability. **Is this compliant to use?** It reads only publicly published business information from a company's own website. You are responsible for lawful use under your jurisdiction. ### Other Actors by this author - **[Company Enrichment API](https://apify.com/renzomacar/company-enrichment)** — full nested company profile. - **[CRM Enrichment](https://apify.com/renzomacar/crm-enrichment)** — flat, CRM-ready firmographics. - **[People Enrichment](https://apify.com/renzomacar/people-enrichment)** — name + company → title, seniority & email. - See all my [Actors](https://apify.com/renzomacar). *** Built by [Renzo Madueño](https://apify.com/renzomacar). Found a bug or want a signal added? Open an **Issue** — I read and respond to every one. # Actor input Schema ## `leads` (type: `array`): One lead per line. Any format: an email (jane@acme.com), a domain (acme.com), or a name + company (Jane Smith, acme.com). ## `targetIndustries` (type: `array`): Boost leads whose industry matches these (e.g. "SaaS", "Fintech", "E-commerce"). Leave empty to score by data completeness only. ## `minEmployeeBand` (type: `string`): Boost leads at/above this size band; penalize below. ## `maxPagesPerDomain` (type: `integer`): Pages crawled per company. More = richer data, slightly slower. ## `minConfidence` (type: `number`): You only pay for results: leads below this confidence (or without substantive data) are returned but NOT charged. ## Actor input object example ```json { "leads": [ "patrick@stripe.com", "hubspot.com", "Amir Salihefendic, doist.com" ], "targetIndustries": [], "maxPagesPerDomain": 5, "minConfidence": 0.3 } ``` # 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 = { "leads": [ "patrick@stripe.com", "hubspot.com", "Amir Salihefendic, doist.com" ], "targetIndustries": [] }; // Run the Actor and wait for it to finish const run = await client.actor("renzomacar/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 = { "leads": [ "patrick@stripe.com", "hubspot.com", "Amir Salihefendic, doist.com", ], "targetIndustries": [], } # Run the Actor and wait for it to finish run = client.actor("renzomacar/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 '{ "leads": [ "patrick@stripe.com", "hubspot.com", "Amir Salihefendic, doist.com" ], "targetIndustries": [] }' | apify call renzomacar/lead-enrichment --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=renzomacar/lead-enrichment", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lead Enrichment & Scoring - Email/Domain to ICP Fit", "version": "0.1", "x-build-id": "kmfNrbHdZYOtgsfOL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/renzomacar~lead-enrichment/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-renzomacar-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/renzomacar~lead-enrichment/runs": { "post": { "operationId": "runs-sync-renzomacar-lead-enrichment", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/renzomacar~lead-enrichment/run-sync": { "post": { "operationId": "run-sync-renzomacar-lead-enrichment", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "leads" ], "properties": { "leads": { "title": "Leads to enrich & score", "type": "array", "description": "One lead per line. Any format: an email (jane@acme.com), a domain (acme.com), or a name + company (Jane Smith, acme.com).", "items": { "type": "string" } }, "targetIndustries": { "title": "Target industries (ICP) — optional", "type": "array", "description": "Boost leads whose industry matches these (e.g. \"SaaS\", \"Fintech\", \"E-commerce\"). Leave empty to score by data completeness only.", "items": { "type": "string" } }, "minEmployeeBand": { "title": "Minimum company size (ICP) — optional", "enum": [ "1-10", "11-50", "51-200", "201-500", "501-1000", "1001-5000", "5000+" ], "type": "string", "description": "Boost leads at/above this size band; penalize below." }, "maxPagesPerDomain": { "title": "Max pages per company", "minimum": 1, "maximum": 12, "type": "integer", "description": "Pages crawled per company. More = richer data, slightly slower.", "default": 5 }, "minConfidence": { "title": "Minimum confidence to charge", "minimum": 0, "maximum": 1, "type": "number", "description": "You only pay for results: leads below this confidence (or without substantive data) are returned but NOT charged.", "default": 0.3 } } }, "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 } } } } } } } } } } ```