Proactively retain customers predicted to churn with this automated n8n workflow. Running daily, it identifies high-risk customers from your Google Sheet, uses Google Gemini to generate personalized win-back offers based on their churn score and preferences, sends these offers via Gmail, and logs all actions for tracking. What does this workflow do? This workflow automates the critical process of customer retention by: Running automatically every day** on a schedule you define. Fetching customer data** from a designated Google Sheet containing metrics like predicted churn scores and preferred categories. Filtering* to identify customers with a high churn risk (score > 0.7) who haven't recently received a specific campaign (based on the created_campaign_date field - *you might need to adjust this logic). Using Google Gemini AI to dynamically generate one of three types of win-back offers, personalized based on the customer's specific churn score and preferred product categories: Informational: (Score 0.7-0.8) Highlights new items in preferred categories. Bonus Points: (Score 0.8-0.9) Offers points for purchases in a target category (e.g., Books).
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GmailGoogle Gemini Chat Model
Google Sheets
Structured Output ParserRelated workflows
See all AI Automation→Find and verify business emails with ScraperCity, OpenAI, Google Sheets, and Gmail
Quick overview This workflow reads contacts from Google Sheets, finds and verifies their business emails via the ScraperCity Email Finder API, generates a short personalized icebreaker with OpenAI, appends the enriched leads to another Google Sheet, and emails a run summary via Gmail. How it works Starts manually to run an email enrichment job. Reads contacts (first name, last name, domain) from an input Google Sheets worksheet and aggregates them into a single payload. Submits the contacts to the ScraperCity Email Finder API, then waits and repeatedly checks the job status until it succeeds or fails/times out. When the job succeeds, downloads the result CSV from ScraperCity, parses it, normalizes key fields (name, domain, email, verification status, provider), and keeps only rows with an email. For each verified contact, prompts OpenAI to generate one short, company-referencing cold-email opening line in the configured language. Appends the enriched lead (including email, status, and icebreaker) to an output Google Sheets worksheet and sends a summary email via Gmail. Setup Create a ScraperCity account with Email Finder credits and add an HTTP Header Auth credential that sends you
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Scrape and score Amazon products with Apify, OpenAI, and Google Sheets
Quick overview This workflow manually scrapes product results from an Amazon search URL via the Apify Amazon Crawler actor, uses OpenAI to generate a structured product analysis (category, key feature, verdict, value score), and appends the enriched results to a Google Sheets spreadsheet. How it works Runs when you manually click “Test workflow” in n8n. Sends the configured Amazon search URL and scraping limits to the apify Amazon Crawler actor and retrieves the dataset items. Filters out any scraper items that are missing a title or contain an error. Uses OpenAI to analyze each product’s title, brand, price, rating, and review count and returns a structured category, key feature, buyer verdict, and 1–10 value score. Combines the scraped fields and AI analysis into a single row format and adds a timestamp. Appends each enriched product as a new row in Google Sheets. Setup Create an apify API token and add it as an HTTP Header Auth credential used by the Apify request. Add an OpenAI API key as an OpenAI credential and choose the model to use in the configuration. Connect a Google Sheets OAuth2 account and select the target spreadsheet, sheet tab, and columns for appending rows. Upda
Route procurement helpdesk tickets from Gmail with Groq and Slack
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