n8n Marketing Automation Workflows for Growth Teams

Monitors target subreddits for posts matching your ICP, classifies them with GPT-4o-mini into lead/content/question categories, scores relevancy, and routes qualified leads to Google Sheets + Slack.

Takes a list of LinkedIn profile URLs from a Google Sheet, enriches each through a data provider API (Proxycurl or RocketReach), extracts job title, company size, and industry, then pushes enriched contacts into HubSpot with custom properties.

Key detail: Use a Function node to map Proxycurl's company.employee_count field into HubSpot's company size property. Set the IF node to filter for companies with 50-500 employees to focus on mid-market leads.

Receives webhook events from your website (page views, form starts, pricing page visits), aggregates them per visitor session using a Function node, calculates a behavioral intent score, and creates a HubSpot contact when the score exceeds your threshold.

Scoring logic: Assign points per action: pricing page = 15, case study = 10, blog = 3, form start = 20, documentation = 5. Sum per session. Threshold of 40 catches visitors who hit pricing + at least one case study.

Listens to HubSpot contact property changes via webhook. When a lead's score crosses defined thresholds (e.g., 30, 60, 90), it triggers the corresponding email sequence in your ESP. Prevents duplicate enrollments by checking a custom HubSpot property before sending.

Fires a formatted Slack message to the appropriate channel whenever a deal moves stages in HubSpot. Maps each pipeline stage to a specific Slack channel (e.g., #new-leads, #proposals, #closed-won) and includes deal value, contact name, and days in previous stage.

Runs daily, queries HubSpot for contacts with no activity in the last 30 days (no email opens, no page views, no form fills). Segments them by original lead source and triggers a tailored re-engagement email via your ESP. Contacts inactive for 90+ days are moved to a dormant lifecycle stage.

Pulls yesterday's spend from Meta Ads and Google Ads APIs, compares against your daily budget target (monthly budget / days in month), and sends a Slack alert if spend is more than 15% over or under pace. Includes remaining budget for the month and projected end-of-month spend.

Fetches ad-level performance data daily, calculates 7-day rolling CTR for each creative, and flags ads where CTR has dropped more than 20% from their 7-day peak. Sends a Slack message listing fatigued creatives with their current vs. peak CTR so you can rotate them before wasting spend.

Every Monday morning, aggregates the previous week's campaign data across Meta and Google Ads, calculates key metrics (spend, impressions, clicks, conversions, CPA, ROAS), appends a new row per campaign to a Google Sheet, and posts a summary to Slack with week-over-week trends.

Pulls data from GA4 (sessions, conversions), HubSpot (new contacts, deals created), and your ad platforms (spend, revenue). Writes everything to a structured Google Sheet that powers a Looker Studio dashboard. One workflow replaces 3 manual data pulls every Monday.

Compares today's metrics against the trailing 7-day average. Alerts on spend spikes (>30% above average), conversion drops (>25% below average), and traffic anomalies. Catches issues like a broken tracking pixel or a campaign accidentally left running over the weekend.

Collects conversion data with UTM parameters from GA4, matches them against ad platform reported conversions, and writes both views to a single Google Sheet. Exposes discrepancies between platform-reported and analytics-reported conversions so you can make informed budget allocation decisions. For a real-world implementation of this approach, see the Alphorm case study.

Searches Reddit (via API) and Twitter/X (via search API or Apify actor) for mentions of your brand name, product name, or founder name. Deduplicates against a Google Sheet log of previously seen post IDs. New mentions get posted to a #brand-mentions Slack channel with the post text, author, and direct link.

Monitors competitor blogs via RSS feeds. When a new post is published, it fetches the full content via HTTP Request, sends it to an OpenAI node for summarization (3-bullet summary + topic classification), and posts the summary to a #competitor-intel Slack channel. Keeps your team aware of competitor positioning without anyone manually checking 10 blogs.

Extends the mention monitoring workflow by passing each mention through an OpenAI node for sentiment analysis (positive, neutral, negative + confidence score). Logs results to Google Sheets with a running weekly sentiment average. Alerts Slack only on negative mentions with high confidence (>0.8) so you can respond quickly to complaints or PR issues.