Marketers and small business owners rely heavily on LinkedIn to find potential customers and initiate conversations. There are hundreds of activities recorded from all the connections that a person has and some of them could be valuable signals leading to sales opportunities. However, it is not possible to keep a track of all the updates from connections on LinkedIn 24x7. That's why we've created this automation workflow using n8n and Google Gemini which helps you execute this task 24x7, automatically so that you don't miss any sales opportunities within your network. Here's the step-by-step guide: Step 1 - Daily trigger - Node: Schedule Trigger, run once at end-of-day. - Input: list of LinkedIn connection IDs (store as credential/variable). --------------- Step 2 - Fetch connection activity - Node: HTTP Request to LinkedIn API / provider. - Pull each connection’s recent updates for the day: posts, comments, likes, shared articles, job changes using LinkedIn's official APIs / SerpApi / ScrapingDog's LinkedIn Scraper API. - Use SplitInBatches to iterate safely and respect rate limits. --------------- Step 3 - Normalize activity items - Node: Set / Function. - Produce structured records: connection_name, profile_url, item_type (post/comment/like), text_snippet, post_url, timestamp, engagement_count. --------------- Step 4 - Summarize daily activity (Gemini) - Node: HTTP Request → Google Gemini. - Prompt: ask Gemini to turn a connection’s activity items into a 1–2 line summary (what they posted, tone, any request/need hinted). - Save Gemini summary per connection. Short example prompt to paste into Gemini: "Summarize these LinkedIn activity items for [name]. Output one short sentence describing what they’re up to today and whether they appear to be seeking vendors, hiring, or sharing product feedback." --------------- Step 5 - Enrich & match to your offering (optional) - Node: HTTP Request to Cura8.ai by navan.ai, Clearbit or Apollo. - Get company industry, size, recent news. - Combine company facts with the activity summary for context.

For certain businesses, new opportunities open up when there's a change in the management or the target lead gets promoted. To find such prospects manually is nearly impossible for any marketing team. Hence, we've created this guide which will help you automate this process using n8n and Generative AI: Step 1 — Define ICP (user input) - Node: Manual Trigger or Form node to collect: target job titles/designations, industries, country/state/city, keyword(s) (e.g., “automation”), seniority level, company size range. - Save ICP as variables for downstream queries. ------------ Step 2 — Search LinkedIn for matches - Node: HTTP Request to LinkedIn API / Sales Navigator / SerpApi People Search. - Query using ICP variables: title keywords OR exact titles, industry, location, company size filters, and keyword in headline/about. - Use pagination + SplitInBatches for rate limits. ------------ Step 3 — Grab promotion signals (profile + posts) - For each candidate, collect available signals: current position start date, previous role timestamps, recent LinkedIn posts mentioning “promoted”, “excited to take on”, “new role”, or job change activity. - If using Sales Navigator/People API, pull position history and activity feed snippets. If using SerpApi, capture profile headline + recent post text. ------------ Step 4 — Use Gemini to classify “promoted in last 3 months” - Node: HTTP Request → Google Gemini API. - Ask Gemini to read profile snippet + position start dates + recent post excerpts and return JSON: {promoted:true/false, promotion_date, confidence, reason_short}. - Threshold: keep only promoted=true AND confidence ≥ 75 (adjustable). Suggested short Gemini prompt (single-line style you can paste): "Classify whether this LinkedIn profile or recent post indicates the person was promoted within the last 3 months. Return JSON: {promoted:true/false, promotion_date:YYYY-MM-DD or null, confidence:0-100, reason}." ------------ Step 5 — Enrich shortlisted prospects (optional)

There are hundreds of virtual events happening on LinkedIn every day. Did you know that a lot of these events allow you to see the other attendees once you RSVP for those events? That's where your potential customers can be found. Curious to know how and want try it for your business? Follow this guide: 1. Trigger - Node: Manual Trigger (so you can run it after RSVPing “Yes” to a webinar). - Input: LinkedIn event URL or event name (e.g., “Automation”). 2. Collect Webinar Attendee Data - Source: LinkedIn event “Networking” tab (accessible through LinkedIn API or a service like SerpApi). - Node: HTTP Request. - Output: list of attendee profiles (name, title, company, LinkedIn URL). - Note: respect LinkedIn’s API policies - don’t scrape unapproved. 3. Structure Attendee Data - Node: Set. - Extract fields: first_name, last_name, title, company, profile_url. - Store them in structured format for later enrichment and message generation. 4. Enrich Profiles (optional) - Node: HTTP Request to Cura8.ai, Clearbit, Apollo, or ZoomInfo. - Add company info: size, industry, funding, headquarters. - This context will improve personalization. 5. Create Hyper-Personalized Messages (GenAI) - Node: HTTP Request → Google Gemini API. - Prompt idea: “You are a marketing professional preparing to reach out to webinar attendees. Write a short, friendly LinkedIn message (3–4 lines) to {{first_name}}, {{title}} at {{company}}, who attended the {{event_name}} webinar. Acknowledge the webinar, show curiosity about their business, and suggest exploring if our offerings could help. Do not pitch aggressively. Output JSON: {message}.” - Gemini returns the personalized message text for each attendee. 6. Log Results - Node: Google Sheets. - Create a sheet with columns: name, title, company, LinkedIn URL, message_text, event_name, date_added. - Append each attendee with their personalized message. 7. Manual Review & Outreach - The marketing professional reviews the Google Sheet. - Copy/paste the hyper-personalized messages into LinkedIn manually (keeps it authentic and avoids automation risks on LinkedIn).

The easiest way to find relevant leads would be to find those who are dissatisfied with your competitors. However, the question is, how can this be done? Here's a step-by-step guide on how you can build your own automation on n8n which uses Generative AI to find leads who are dissatisfied with your competitors: Step 1 - Trigger & Input - Node: Schedule Trigger (or Manual Trigger). - Input: competitor business name or Google Place ID (single or batch). ------------------------------ Step 2 - Fetch Google Maps Reviews - Node: HTTP Request to Google Places Details (or provider API). - Pull the latest reviews (author_name, review_text, rating, time). - Keep review metadata: review_id, place_id, reviewer name, review text, rating, review_date, review_url ------------------------------ Step 3 - Filter reviews that indicate dissatisfaction (Gemini) - Node: HTTP Request → Google Gemini. - Prompt (concise): classify whether the review indicates the reviewer is dissatisfied and potentially open to an alternative. Ask Gemini to return JSON: {dissatisfied: true/false, confidence: 0-100, reason}. - Filter Node: keep only reviews with dissatisfied=true and confidence >= 70. ------------------------------ Step 4 - Normalize reviewer names - Node: Function / Set. - Extract first_name, last_name from author_name; store original author_name for logging and matching. ------------------------------ Step 5 - Search LinkedIn for the reviewer name - Node: HTTP Request to SerpApi LinkedIn People Search OR use LinkedIn People API / Apollo / ZoomInfo search. - Query: name + location. - Output: list of candidate LinkedIn profile URLs and snippet info (headline, current_company, location). ------------------------------ Step 6 - Match prospect profiles to review (Gemini) - Node: HTTP Request → Gemini per prospect (or batch). - Prompt (concise): given the review text, competitor business, and a prospect LinkedIn profile snippet (headline, current company, bio), decide match quality and output JSON: {match: true/false, score:0-100, why}. Ask Gemini to prefer prospects whose company/role or review content contextually aligns with the review (e.g., the reviewer references being a customer, an ex-employee, local resident, or having the role that would use/buy that product). - Keep only candidates with match=true and score >= 75.

Attending business conferences and exhibitions to generate new leads? Why should you limit your lead generation efforts to the event networking app or in-person connections at the event? Business events have a gathering of hundreds or thousands of people. It is easier to generate more leads if you can find who's attending and reach out to them in advance to meet at the event. Here's a n8n automation which uses GenAI to find such prospects and reach out to them by crafting hyper-personalized emails to book meetings: 1. Trigger - Node: Manual Trigger (for ad-hoc runs) or Schedule Trigger (if you want it to refresh periodically). - Input variable: Event name (e.g. “GITEX 2025”). 2. Collect LinkedIn Posts - Option A: Use SerpApi LinkedIn search (safer than raw scraping). - Option B: If you have LinkedIn API access, query posts mentioning the event. - Node: HTTP Request. - Query: "Event Name" (e.g. “GITEX 2025”) with filters for last 30 days. 3. Filter for Attendees (GenAI) - Node: HTTP Request → Google Gemini API. - Prompt: “You are analyzing LinkedIn post text. Decide if the author is attending the event mentioned. Output JSON: {attending: true/false, confidence: 0-100, reasoning}.” - Filter Node: Keep only posts where attending=true and confidence >70. 4. Extract Prospect Details - From the LinkedIn post metadata: person’s name, designation, company. - Node: Set → structure this into fields: first_name, last_name, title, company_name. 5. Find Email IDs - Node: HTTP Request to Apollo or ZoomInfo. - Input: name + company. - Output: validated business email addresses. 6. Enrich Company Context (optional) - Node: HTTP Request to Clearbit, Cura8.ai or Crunchbase. - Fetch company size, industry, funding, or recent news. - This data helps make outreach more specific. 7. Craft Outreach (GenAI) - Node: HTTP Request → Google Gemini API. - Prompt example: - System: “You are a sales assistant creating short, hyper-personalized cold emails.” - User: “Write a plain-text email to {{first_name}}, {{title}} at {{company}} who is attending {{event_name}}. The sender sells {{your_solution}}. Suggest meeting at the event. Use company context: {{company_industry}}, {{funding}}, {{size}}. Output JSON: {subject, body, cta}.”