by Hueston
Who is this for? Sales professionals looking to build lead lists from target company domains Business development teams conducting outreach campaigns Marketers building contact databases for account-based marketing Recruiters searching for potential candidates at specific companies Anyone needing to transform a list of company domains into actionable contact information What problem is this workflow solving? Finding business email addresses for outreach is a time-consuming process. The Apollo API doesn't provide a direct way to extract email contacts from domains in a single call. This workflow bridges that gap by: Automating the two-step process required by Apollo's API Processing multiple domains in batches without manual intervention Extracting, enriching, and storing contact information in a structured format Eliminating hours of manual data entry and API interaction What this workflow does This workflow creates an automated pipeline between Google Sheets and Apollo's API to: Pull a list of target domains from a Google Sheet Submit each domain to Apollo's search API to find associated people Loop through each person found and enrich their profile data Extract key information: name, title, email address, and LinkedIn URL Write the enriched contact information back to a results sheet Process the next domain automatically until all are complete Setup Prerequisites: An n8n instance (cloud or self-hosted) Apollo.io account with API access Google account with access to Google Sheets Google Sheets Setup: Create a new Google Sheet with two tabs: Tab 1: "Target Domains" with a column named "Domain To Enrich" Tab 2: "Results" with columns: Company, First Name, Last Name, Title, Email, LinkedIn n8n Setup: Import the workflow JSON into your n8n instance Set up Google Sheets credentials in n8n Update the Google Sheets document ID in both Google Sheets nodes Add your Apollo API key to both HTTP Request nodes Review and adjust API rate limits if needed Testing: Add a few test domains to your "Target Domains" sheet Run the workflow manually to verify it's working correctly Check the "Results" sheet to confirm data is being properly populated How to customize this workflow to your needs Adding More Contact Fields: Modify the "Clean Up" node to extract additional fields from the Apollo API response Add corresponding columns to your "Results" sheet Update the "Results To Results Sheet" node mapping to include the new fields Filtering Results: Add a Filter node after "Clean Up" to include only contacts with specific roles Create conditions based on title, seniority, or other fields returned by Apollo Automating Workflow Execution: Replace the manual trigger with a Schedule Trigger to run daily/weekly Add a Filter node to process only domains with "Not Processed" status Update the status field in Google Sheets after processing Additional Notes This workflow respects Apollo's API rate limits by processing one contact at a time The Apollo API may not return contact information for all domains or all employees Consider legal and privacy implications when collecting and storing contact information Made with ❤️ by Hueston
by Yaron Been
Automated system to track and analyze technology stacks used by target companies, helping identify decision-makers and technology trends. 🚀 What It Does Tracks technology stack of target companies Identifies key decision-makers (CTOs, Tech Leads) Monitors technology changes and updates Provides competitive intelligence Generates actionable insights 🎯 Perfect For B2B SaaS companies Technology vendors Sales and business development teams Competitive intelligence analysts Market researchers ⚙️ Key Benefits ✅ Identify potential customers ✅ Stay ahead of technology trends ✅ Target decision-makers effectively ✅ Monitor competitor technology stacks ✅ Data-driven sales strategies 🔧 What You Need BuiltWith API key n8n instance CRM integration (optional) Email/Slack for alerts 📊 Data Tracked Company technologies Hosting providers Frameworks and libraries Analytics tools Marketing technologies 🛠️ Setup & Support Quick Setup Deploy in 20 minutes with our step-by-step guide 📺 Watch Tutorial 💼 Get Expert Support 📧 Direct Help Gain a competitive edge by understanding the technology landscape of your target market.
by Airtop
Scoring LinkedIn Profiles Against Your ICP Use Case This automation scores individual LinkedIn profiles against your Ideal Customer Profile (ICP) based on interest in AI, technical depth, and seniority level. It's ideal for prioritizing leads and understanding how well a person fits your ICP criteria. What This Automation Does Given a LinkedIn profile and an Airtop profile, it: Extracts relevant data from the person's profile Determines levels of AI interest, seniority, and technical depth Calculates an ICP score based on weighted criteria Returns the full enriched profile with the score Input parameters: LinkedIn Profile URL** (e.g., https://linkedin.com/in/janedoe) Airtop Profile** connected to LinkedIn ICP scoring method** in the Airtop node prompt Output fields in JSON format: Full name, job title, employer, company LinkedIn URL, location, number of connections and followers, about section content and more Calculated ICP Score (out of 100) How It Works Form Trigger or Workflow Trigger: Accepts input from either a form or another workflow. Parameter Assignment: Ensures proper variable names for downstream nodes. Airtop Enrichment Tool: Extracts and scores the person based on a detailed prompt. Scoring: Uses this point system: AI Interest: beginner (5), intermediate (10), advanced (25), expert (35) Technical Depth: basic (5), intermediate (15), advanced (25), expert (35) Seniority Level: junior (5), mid-level (15), senior (25), executive (30) Output Formatting: Cleans and returns the result as JSON. Setup Requirements IMPORTANT: Enter your ICP scoring method in the prompt field of the Airtop node Airtop Profile connected to LinkedIn. Airtop API credentials configured in n8n. Optional: a front-end form to collect profile URLs and trigger the automation. Next Steps Embed in CRM**: Trigger this automation on new leads to auto-score them. Batch Process Leads**: Run it over a list of profile URLs for segmentation. Customize Scoring**: Adjust point weights based on your sales priorities. Read more about Scoring LinkedIn Profiles Against Your ICP
by n8n Team
This workflow syncs Shopify customers to your HubSpot account as contacts. Whenever somebody makes a purchase on Shopify, it automatically adds them as a new customer to your Hubspot account if the customer doesn’t exist yet. Also, this workflow creates or updates contacts from new paid orders on Shopify by adding the amount and order close date of the deal. Prerequisites Shopify account and Shopify credentials HubSpot account and HubSpot credentials How it works Shopify trigger starts the workflow whenever an order is updated. HubSpot node creates or updates the contact who made the order update. Set node sorts and passes only the userid. Merge node merges data of both inputs, the order and the customer. Hubspot node looks up if the order already exists. If node splits the workflow conditionally, based on data received. If the order is new, the new deal is created in the Hubspot node.
by Not Another Marketer
You Don’t Need More Tools. You Just Need the Right Leads. Why spend $1,000s on lead gen when your perfect leads are already waiting in Apollo? You’ve already filtered the ideal prospects. You know who they are, where they work, and what they do. Now imagine turning that list into enriched, ready-to-contact leads—without paying pricey Apollo's recurring subscription (spoiler: you will pay only 0.60$ per 500 leads). From Filter to Outreach-Ready in Seconds With the Lead Generation System, you just drop your Apollo search URL. The workflow does the rest: ✅ Scrapes all matching contacts from your Apollo filter ✅ Enriches and organizes the data (names, roles, emails, LinkedIns, companies, etc.) ✅ Delivers the final lead list to Airtable—or your CRM of choice No more manual exports. No CSV mess. No VA needed. Just qualified leads, cleaned and ready to go. Perfect For Founders doing DIY outbound Growth marketers scaling cold email Agencies running lead-gen for clients Anyone tired of paying too much for messy, outdated lists Setup Guide I built a step-by-step guide to setup this workflow in 5 to 10 minutes, available here: https://notanothermarketer.gitbook.io/home/templates/lead-generation This template is free. Enjoy!
by Airtop
Scoring LinkedIn Profiles Against Your ICP Use Case This automation scores individual LinkedIn profiles against your Ideal Customer Profile (ICP) based on interest in AI, technical depth, and seniority level. It's ideal for prioritizing leads and understanding how well a person fits your ICP criteria. What This Automation Does Given a LinkedIn profile and an Airtop profile, it: Extracts relevant data from the person's profile Determines levels of AI interest, seniority, and technical depth Calculates an ICP score based on weighted criteria Returns the full enriched profile with the score Input parameters: LinkedIn Profile URL** (e.g., https://linkedin.com/in/janedoe) Airtop Profile** connected to LinkedIn ICP scoring method** in the Airtop node prompt Output fields in JSON format: Full name, job title, employer, company LinkedIn URL, location, number of connections and followers, about section content and more Calculated ICP Score (out of 95) How It Works Form Trigger or Workflow Trigger: Accepts input from either a form or another workflow. Parameter Assignment: Ensures proper variable names for downstream nodes. Airtop Enrichment Tool: Extracts and scores the person based on a detailed prompt. Scoring: Uses this point system: AI Interest: beginner (5), intermediate (10), advanced (25), expert (35) Technical Depth: basic (5), intermediate (15), advanced (25), expert (35) Seniority Level: junior (5), mid-level (15), senior (25), executive (30) Output Formatting: Cleans and returns the result as JSON. Setup Requirements IMPORTANT: Enter your ICP scoring method in the prompt field of the Airtop node Airtop Profile connected to LinkedIn. Airtop API credentials configured in n8n. Optional: a front-end form to collect profile URLs and trigger the automation. Next Steps Embed in CRM**: Trigger this automation on new leads to auto-score them. Batch Process Leads**: Run it over a list of profile URLs for segmentation. Customize Scoring**: Adjust point weights based on your sales priorities. Read more about ICP Scoring with Airtop and n8n
by Ricardo Espinozaas
Use Case When tracking your contacts and leads in Hubspot CRM, every new contact might be a potential customer. To guarantee that you're keeping the overview you'd normally need to look at every new lead that is coming in manually to identify high-quality leads to prioritize their engagement and optimize the sales process. This workflow saves the work and does it for you. What this workflow does The workflow runs every 5 minutes. On every run, it checks the Hubspot CRM for contacts that were added since the last check. It then checks if they meet certain criteria (in this case if they are making +5m annual revenue) and alerts you in Slack for every match. Setup Add Hubspot, and Slack credentials. Click on Test workflow. How to adjust this workflow to your needs Change the schedule interval Adjust the criteria to send alerts
by Nick Saraev
AI LinkedIn Outreach Automation with Apollo, OpenAI & PhantomBuster Categories:* Sales Automation Lead Generation AI Personalization This workflow creates a complete LinkedIn outreach automation system that generates targeted lead lists from Apollo using natural language, enriches profiles with AI-personalized icebreakers, and automatically sends connection requests through PhantomBuster. Built by someone who's made over $1 million with AI automation, this system demonstrates the real-world approach to building profitable automation workflows. Benefits* Natural Language Lead Targeting - Describe your ideal prospects in plain English and automatically generate Apollo search URLs AI-Powered Personalization - Creates custom icebreakers based on LinkedIn profile data, employment history, and professional background Complete Outreach Pipeline - From lead discovery to personalized connection requests, fully automated end-to-end Smart Data Management - Automatically tracks all prospects in Google Sheets with deduplication and status tracking Cost-Effective Scraping - Uses Apify to extract Apollo data without expensive subscription costs Scalable Architecture - Processes hundreds of leads while respecting LinkedIn's connection limits How It Works* Natural Language Lead Generation: Form input accepts audience descriptions in plain English AI converts descriptions into properly formatted Apollo search URLs Automatically includes location, company size, job titles, and keyword filters Apollo Data Extraction: Uses Apify actor to scrape targeted lead lists from Apollo Extracts LinkedIn URLs, email addresses, employment history, and profile data Processes 500+ leads per run with detailed professional information AI Personalization Engine: Analyzes LinkedIn profile data including job history and company information Generates personalized icebreakers using proven connection request templates Creates human-like messages that reference specific career details and achievements Google Sheets Integration: Automatically stores all lead data in organized spreadsheet format Tracks prospect information, contact details, and generated icebreakers Provides easy data management and campaign tracking PhantomBuster Automation: Connects to PhantomBuster API to trigger LinkedIn connection campaigns Sends personalized connection requests with custom icebreakers Respects LinkedIn's daily limits and mimics human behavior patterns Business Use Cases* Sales Teams - Automate prospecting for B2B outreach campaigns Agencies - Scale client acquisition through targeted LinkedIn outreach Recruiters - Find and connect with qualified candidates efficiently Entrepreneurs - Build professional networks in specific industries Business Development - Generate qualified leads for partnership opportunities Revenue Potential This system can replace expensive LinkedIn outreach tools that cost $200-500/month. Users typically see: 400% improvement in response rates through personalization 10x faster lead generation compared to manual prospecting Ability to process 500+ leads per hour vs. 10-20 manually Difficulty Level: Intermediate Estimated Build Time: 1-2 hours Monthly Operating Cost: ~$50 (Apollo + PhantomBuster + AI APIs) Watch My Complete 1-Hour Build* Want to see exactly how I built this system from scratch? I walk through the entire development process live, including all the debugging, API integrations, and real-world testing that goes into building profitable automation systems. 🎥 See My Live Build Process: "Build This Automated AI LinkedIn DM System in 1 Hour (N8N)" This comprehensive tutorial shows my actual development approach - including the detours, problem-solving, and iterative testing that real automation building involves. Required Google Sheets Setup* Create a Google Sheet with these exact column headers: Essential Lead Columns: id - Unique prospect identifier first_name - Contact's first name last_name - Contact's last name name - Full name linkedin_url - LinkedIn profile URL title - Current job title email_status - Email verification status photo_url - Profile photo URL icebreaker - AI-generated personalized message Setup Instructions: Create Google Sheet with these headers in row 1 Connect Google Sheets OAuth in n8n Update the document ID in the "Add to Google Sheet" node PhantomBuster will read from this sheet for automated outreach Set Up Steps* Apollo & Apify Configuration: Set up Apify account and obtain API credentials Configure Apollo scraper actor with proper parameters Test lead extraction with sample audience descriptions AI Personalization Setup: Configure OpenAI API for natural language processing and personalization Set up prompt templates for audience targeting and icebreaker generation Test personalization quality with sample LinkedIn profiles Google Sheets Integration: Create lead tracking spreadsheet with proper column structure Configure Google Sheets API credentials and permissions Set up data mapping for automatic lead storage PhantomBuster Connection: Set up PhantomBuster account and LinkedIn connection Configure LinkedIn auto-connect agent with custom message templates Connect API for automated campaign triggering Form and Workflow Setup: Configure form trigger for audience input collection Set up data flow between all components Add proper error handling and rate limiting Testing and Optimization: Start with small batches (5-10 connections daily) Monitor LinkedIn account health and response rates Optimize icebreaker templates based on performance data Important Compliance Notes* LinkedIn Limits: Respect 100 connection requests per week limit Account Safety: Use PhantomBuster's human-like behavior patterns Message Quality: Regularly update templates to avoid automation detection Response Management: Monitor and respond to replies within 24 hours Advanced Extensions* This system can be enhanced with: Multi-channel Outreach: Add email sequences for comprehensive campaigns A/B Testing: Test different icebreaker templates automatically CRM Integration: Connect to Salesforce, HubSpot, or other sales systems Response Tracking: Monitor reply rates and optimize messaging Explore My Channel* For more advanced automation systems that generate real business results, check out my YouTube channel where I share the exact strategies I've used to make over $1 million with AI automation.
by Airtop
Automating Company ICP Scoring via LinkedIn Use Case This automation scores companies based on their LinkedIn profile using custom Ideal Customer Profile (ICP) criteria. It’s ideal for qualifying B2B leads and prioritizing outreach based on fit. What This Automation Does Inputs required: Company LinkedIn URL**: Public LinkedIn profile of the company. Airtop Profile (connected to LinkedIn)**: Airtop Profile authenticated to access and extract profile data. The automation analyzes the LinkedIn page and calculates a score based on: Scoring Criteria | Category | Classification | Points | |--------------------|---------------------------|------------| | AI Focus | Low | 5 | | | Medium | 10 | | | High | 25 | | Technical Level | Basic | 5 | | | Intermediate | 15 | | | Advanced | 25 | | | Expert | 35 | | Employee Count | 0–9 | 5 | | | 10–150 | 25 | | | 150+ | 30 | | Agency Status | Not Automation Agency | 0 | | | Automation Agency | 20 | | Geography | Outside US/Europe | 0 | | | US/Europe Based | 10 | The result includes: Total ICP score Detailed justifications for each score component How It Works Opens the company’s LinkedIn page using Airtop. Analyzes metadata including employee count, headquarters, services, and keywords. Applies the scoring rubric and returns structured JSON with scores and reasons. Optionally flattens the result for storage or CRM integration. Setup Requirements Airtop API Key LinkedIn-authenticated Airtop Profile Next Steps Combine with Lead Lists**: Score companies from outreach lists. Push to CRM**: Add scores to HubSpot or Salesforce records. Adjust Scoring Weights**: Modify rubric to reflect your ICP strategy. Read more about company ICP scoring automation with Airtop and n8n
by PollupAI
Who is this for? This workflow is designed for Customer Success Managers (CSM), sales, support, or marketing teams using HubSpot CRM who want to automate customer engagement tracking when new emails arrive. It’s ideal for businesses looking to streamline CRM updates without manual data entry. Problem Solved / Use Case Manually logging email interactions in HubSpot is time-consuming. This workflow automatically parses incoming emails, checks if the sender exists in HubSpot, and either: Creates a new contact + logs the email as an engagement (if the sender is new). Logs the email as an engagement for an existing contact. What This Workflow Does Triggers when a new email arrives in a connected IMAP inbox. Parses the email using AI (OpenAI) to extract structured data. Searches HubSpot for the sender’s email address. Updates HubSpot: Creates a contact (if missing) and logs the email as an engagement. Or logs the engagement for an existing contact. Setup Configure Email Account: Replace the default IMAP node with your email provider HubSpot Credentials: Add your HubSpot API key in the HubSpot nodes. OpenAI Integration: Ensure your OpenAI API key is set for email parsing. Customization Tips Improve AI Prompt**: Modify the OpenAI prompt to extract specific email data (e.g., customer intent). Add Filters**: Exclude auto-replies or spam by adding a filter node. Extend Functionality**: Use the parsed data to trigger follow-up tasks (e.g., Slack alerts, tickets). Need Help? Contact thomas@pollup.net for workflow modifications or help. Discover my other workflows here
by Trey
This workflow will archive your Spotify Discover Weekly playlist to an archive playlist named "Discover Weekly Archive" which you must create yourself. If you want to change the name of the archive playlist, you can edit value2 in the "Find Archive Playlist" node. It is configured to run at 8am on Mondays, a conservative value in case you forgot to set your GENERIC_TIMEZONE environment variable (see the docs here). Special thanks to erin2722 for creating the Spotify node and harshil1712 for help with the workflow logic. To use this workflow, you'll need to: Create then select your credentials in each Spotify node Create the archive playlist yourself Optionally, you may choose to: Edit the archive playlist name in the "Find Archive Playlist" node Adjust the Cron node with an earlier time if you know GENERIC_TIMEZONE is set Setup an error workflow like this one to be notified if anything goes wrong
by Danielle Gomes
Automatically classify incoming leads based on the sentiment of their message using Google Gemini, store them in Supabase by category, and send tailored WhatsApp messages via the official WhatsApp Cloud API. ✅ Use Case: This workflow is ideal for sales, onboarding, and customer support teams who want to: Understand the tone and urgency of each lead Prioritize hot leads instantly Send smart, automatic WhatsApp replies based on user sentiment 🧠 How it works: Capture lead via a Typeform webhook Clean and structure the data (name, email, message, etc.) Run sentiment analysis using Google Gemini to classify the message as: Positive → Hot Lead Neutral → Warm Lead Negative → Cold Lead Store lead data in Supabase under the corresponding category Merge data to unify flow paths Send WhatsApp message using the official WhatsApp Cloud API, with a custom reply for each sentiment result 🔧 Tools used: Typeform (incoming data) Google Gemini (AI-based sentiment classification) Supabase (database) WhatsApp Cloud API (response automation) 🏷 Tags: AI, Sentiment Analysis, Lead Qualification, Supabase, WhatsApp, Gemini, Typeform, CRM, Automation, Customer Engagement