# LinkedIn Audience Email Finder ยท No Cookies (`data-slayer/linkedin-audience-email-finder-no-cookies`) Actor Enter any LinkedIn profile URL โ†’ get every person who engaged with their content, enriched with full profile data and verified work emails. Engagement frequency scoring ranks repeat engagers as warmest leads. No cookies, no login, no subscription. Trigify & Jungler alternative. - **URL**: https://apify.com/data-slayer/linkedin-audience-email-finder-no-cookies.md - **Developed by:** [Data Slayer](https://apify.com/data-slayer) (community) - **Categories:** Lead generation, Social media - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $18.00 / 1,000 enriched leads 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 ### ๐Ÿ“ฌ What is LinkedIn Audience Email Finder? **LinkedIn Audience Email Finder** discovers all recent posts from any LinkedIn profile, extracts every person who engaged โ€” commenters, reactors, reposters โ€” enriches each one with full LinkedIn profile data, and optionally finds and verifies their work email. The result: a ranked lead list sorted by **engagement frequency**. Someone who commented on 7 out of 20 posts is a warmer lead than someone who liked once. No other tool gives you this ranking. No LinkedIn cookies. No login. No Chrome extension. No subscription. Pay only for results. ### โšก How it works 1. **Paste** any LinkedIn profile URL โ€” a competitor, an industry voice, a hiring manager, anyone 2. **Choose** a timeframe โ€” last 7, 14, 30, 60, or 90 days 3. **Select** which engagers to extract โ€” commenters, reactors, reposters, or all three 4. **Toggle** email enrichment on or off 5. **Get** a deduplicated, ranked lead list with engagement intelligence + verified emails The actor automatically discovers all posts within your timeframe, extracts every engager across all posts, deduplicates people who appeared multiple times, scores them by engagement frequency, enriches their full LinkedIn profile, and finds + verifies their work email. ### ๐ŸŽฏ Engagement Frequency Scoring โ€” The Feature Nobody Else Has This is what makes this actor different from every other LinkedIn scraper. When someone engages with a profile's content **repeatedly**, they're not a stranger โ€” they're paying attention. This actor tracks: - **Engagement count** โ€” how many times this person engaged across all posts - **Unique posts engaged** โ€” how many different posts they appeared on - **Engagement types** โ€” did they comment, react, repost, or all three? - **Posts engaged with** โ€” which specific post URLs they engaged on Leads are sorted by engagement count descending. The most active audience members โ€” the ones who show up post after post โ€” are at the top of your list. **Trigify and Jungler don't surface this metric.** They show you who engaged, but not how often. This actor does. --- ### ๐Ÿ”„ How this compares to Trigify, Jungler & Clay | | Trigify | Jungler | Clay | This Actor | |---|---|---|---|---| | Monthly cost | From $149/mo | From $99/mo | From $149/mo | Pay per run | | Engagement frequency scoring | No | No | No | **Yes** | | Verified emails included | Add-on cost | No | Add-on cost | Included | | LinkedIn account required | Yes | Yes | No | No | | Cookies / login needed | Yes | Yes | No | No | | Timeframe control | Fixed | Last 30 days only | N/A | **7, 14, 30, 60, or 90 days** | | Contract | Annual | Monthly | Monthly | None | | What you get | Engagement signals | Engagement monitoring | Enrichment only | **Extraction + frequency scoring + enrichment + emails** | One extraction that would cost you a month's subscription on Trigify costs **$34.50 here**. Run it when you need it. Stop when you don't. ### ๐Ÿ’ฐ Pricing Two-component pricing โ€” you only pay for what you get: | Component | Price | When it's charged | |---|---|---| | **Lead enriched** (base) | $25 / 1,000 leads | Every lead in the output | | **Verified email found** | $29 / 1,000 emails | Only when a valid/catch-all email is found | **Example:** A profile with 1,000 unique engagers across their last 30 days of posts, where 500 verified emails are found โ†’ $25 + $14.50 = **$39.50 total**. Emails disabled? You pay only the $25/1K base for full enriched profiles + engagement scoring. No monthly subscription. No minimum. No wasted credits. No seat licenses. ### ๐Ÿ“Š What you get per lead **Always included (base):** - Full name, first name, last name - LinkedIn profile URL, Sales Navigator URL - Current job title, company, industry, company size, company website - Headline, about/description - Location, country - Connections, followers - Profile flags (premium, creator, job seeker) - **Engagement count** โ€” times they engaged across all posts - **Unique posts engaged** โ€” number of distinct posts - **Engagement types** โ€” commented, reacted, reposted - **Posts engaged with** โ€” list of post URLs - **Engagement details** โ€” per-post breakdown with comment text, reaction type, timestamps - **Source profile URL** โ€” which profile this lead came from - Post text, post author - Complete experience history (all roles, not just current) - Education history - Skills list - Certifications, languages, volunteering, publications, projects **With email enrichment enabled:** - Verified work email address - MillionVerifier verification result (ok, catch-all, invalid, unknown) - MillionVerifier quality score ### ๐ŸŽฏ Use cases - **Steal a competitor's audience** โ€” Enter their LinkedIn profile, extract everyone engaging with their content, reach out with a better offer - **Warm outbound at scale** โ€” People who comment on industry content are 10x more likely to respond than cold leads. Repeat engagers are even warmer. - **ABM signal intelligence** โ€” See which accounts are actively engaging with competitor content - **Partnership prospecting** โ€” Find the most engaged people in an industry voice's audience for co-marketing, sponsorship, or partnerships - **Recruitment** โ€” Find active professionals engaging with industry content in your hiring niche. Someone commenting on hiring posts is already thinking about a move. - **Event follow-up** โ€” Extract everyone who engaged with a speaker's recap posts - **Content creators** โ€” Identify your most loyal audience members and build direct relationships - **Agency lead gen** โ€” Run this for clients' competitors and deliver warm leads monthly ### ๐Ÿ“ฅ Input | Field | Description | |---|---| | **LinkedIn profile URL** | Any LinkedIn profile URL (required) | | **Timeframe** | Last 7, 14, 30, 60, or 90 days | | **Extract commenters** | Include people who commented | | **Extract reactors** | Include people who reacted (liked, celebrated, etc.) | | **Extract reposters** | Include people who reposted | | **Find verified work emails** | Discover and verify work emails | ### ๐Ÿ“ค Output Results are saved to the default Apify dataset. Download as JSON, CSV, or Excel. **JSON output:** Clean top-level fields + nested arrays for experience, education, skills, engagement details. **CSV output:** All top-level fields as clean columns. Nested arrays auto-flatten into trailing columns โ€” your key fields are always in the first columns. #### Sample output (JSON) ```json { "full_name": "Sarah Chen", "first_name": "Sarah", "last_name": "Chen", "email": "sarah.chen@acme.com", "millionverifier_result": "ok", "millionverifier_quality": "good", "job_title": "VP of Sales", "current_company_name": "Acme Corp", "company_industry": "Software Development", "company_website": "https://www.acme.com", "location": "San Francisco, California", "profile_link": "https://www.linkedin.com/in/sarahchen", "connections": 2500, "followers": 1200, "engagement_count": 5, "unique_posts_engaged": 4, "engagement_type": "commented", "posts_engaged_with": ["https://www.linkedin.com/feed/update/..."], "source_profile_url": "https://www.linkedin.com/in/targetprofile", "experience": [...], "education": [...], "skills": [...] } ```` ### โ“ FAQ **Does this require my LinkedIn account or cookies?** No. Completely cookieless. Your LinkedIn account is never touched. Unlike PhantomBuster or browser extensions, no login credentials are needed. **How is this different from LinkedIn Post Engagers Email Finder?** [LinkedIn Post Engagers Email Finder](https://apify.com/data-slayer/linkedin-post-to-verified-leads) extracts engagers from a single post URL you provide. This actor takes any LinkedIn profile URL and automatically discovers all their recent posts within your chosen timeframe, extracts every engager across all of them, deduplicates, and ranks by engagement frequency. You give it a person โ€” it maps their entire engaged audience. **How long does a run take?** Depends on the number of posts and engagers. A typical profile (last 30 days, ~1,000 unique engagers) with emails takes 15โ€“30 minutes. Without emails: 5โ€“10 minutes. **How is this different from Trigify or Jungler?** Trigify and Jungler are subscription monitoring platforms ($99โ€“549/month) that show you engagement signals but don't include verified emails or engagement frequency scoring. This actor gives you audience extraction + full profile enrichment + verified emails + frequency ranking for $20โ€“35 per 1,000 leads, with no subscription. Run it once or schedule it weekly โ€” your choice. **What's the email hit rate?** Typically 40โ€“70%. Higher for people at mid-to-large companies with established domains. Lower for freelancers and very small companies. **What if a profile has thousands of engagers?** The actor processes all unique engagers within your selected timeframe. For high-volume profiles, consider a shorter timeframe (7 or 14 days) to control costs. **Can I use this for ongoing monitoring?** This actor does a one-time extraction. For ongoing monitoring, schedule it on Apify to run weekly or monthly โ€” each run captures new engagers. Apify's built-in scheduling makes this a lightweight alternative to Trigify/Jungler's always-on monitoring at a fraction of the cost. **How are emails verified?** Every email found goes through MillionVerifier's real-time verification API. You get the verification result (ok, catch-all, invalid, unknown) and quality score directly in the output. You only pay the email pricing tier when a valid or catch-all email is found โ€” invalid results don't cost you. ### ๐Ÿ”— Related actors from Data Slayer - [LinkedIn Post Engagers Email Finder](https://apify.com/data-slayer/linkedin-post-to-verified-leads) โ€” Extract engagers from a single LinkedIn post URL with verified emails - [LinkedIn Profile Scraper + Verified Email](https://apify.com/data-slayer/linkedin-profile-scraper) โ€” Scrape full profiles from profile URLs with optional verified emails - [LinkedIn Company Scraper](https://apify.com/data-slayer/linkedin-company-scraper) โ€” Scrape company pages - [LinkedIn Post Analytics Scraper](https://apify.com/data-slayer/linkedin-post-analytics-scraper) โ€” Get post engagement analytics - [LinkedIn Profile Posts Scraper](https://apify.com/data-slayer/linkedin-profile-posts-scraper) โ€” Scrape posts from any LinkedIn profile # Actor input Schema ## `linkedin_profile_url` (type: `string`): Public profile URL (linkedin.com/in/โ€ฆ) or Sales Navigator lead URL. Posts from this profileโ€™s activity feed in the selected timeframe are scanned for engagers. ## `timeframe` (type: `string`): Only posts published within this window (UTC) are included when collecting the thought leaderโ€™s posts. ## `include_commenters` (type: `boolean`): Include people who commented on the influencerโ€™s posts ## `include_reactors` (type: `boolean`): Include people who reacted to the thought leaderโ€™s posts ## `include_reposters` (type: `boolean`): Include people who reposted the thought leaderโ€™s posts ## `find_verified_work_emails` (type: `boolean`): Unchecked: full LinkedIn profile + engagement columns only. Checked: also discover work emails and run SMTP verification. ## Actor input object example ```json { "timeframe": "last_30_days", "include_commenters": false, "include_reactors": false, "include_reposters": false, "find_verified_work_emails": true } ``` # Actor output Schema ## `results` (type: `string`): Dataset rows with profile and email fields as configured # 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("data-slayer/linkedin-audience-email-finder-no-cookies").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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("data-slayer/linkedin-audience-email-finder-no-cookies").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call data-slayer/linkedin-audience-email-finder-no-cookies --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data-slayer/linkedin-audience-email-finder-no-cookies", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Audience Email Finder ยท No Cookies", "description": "Enter any LinkedIn profile URL โ†’ get every person who engaged with their content, enriched with full profile data and verified work emails. Engagement frequency scoring ranks repeat engagers as warmest leads. No cookies, no login, no subscription. Trigify & Jungler alternative.", "version": "1.0", "x-build-id": "vU1NiyOwqCUstMrvr" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data-slayer~linkedin-audience-email-finder-no-cookies/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data-slayer-linkedin-audience-email-finder-no-cookies", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data-slayer~linkedin-audience-email-finder-no-cookies/runs": { "post": { "operationId": "runs-sync-data-slayer-linkedin-audience-email-finder-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data-slayer~linkedin-audience-email-finder-no-cookies/run-sync": { "post": { "operationId": "run-sync-data-slayer-linkedin-audience-email-finder-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "linkedin_profile_url" ], "properties": { "linkedin_profile_url": { "title": "LinkedIn profile URL", "type": "string", "description": "Public profile URL (linkedin.com/in/โ€ฆ) or Sales Navigator lead URL. Posts from this profileโ€™s activity feed in the selected timeframe are scanned for engagers." }, "timeframe": { "title": "Timeframe", "enum": [ "last_7_days", "last_14_days", "last_30_days", "last_60_days", "last_90_days" ], "type": "string", "description": "Only posts published within this window (UTC) are included when collecting the thought leaderโ€™s posts.", "default": "last_30_days" }, "include_commenters": { "title": "Extract commenters", "type": "boolean", "description": "Include people who commented on the influencerโ€™s posts", "default": false }, "include_reactors": { "title": "Extract reactors", "type": "boolean", "description": "Include people who reacted to the thought leaderโ€™s posts", "default": false }, "include_reposters": { "title": "Extract reposters", "type": "boolean", "description": "Include people who reposted the thought leaderโ€™s posts", "default": false }, "find_verified_work_emails": { "title": "Find verified work emails", "type": "boolean", "description": "Unchecked: full LinkedIn profile + engagement columns only. Checked: also discover work emails and run SMTP verification.", "default": 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 } } } } } } } } } } ```