This article provides a complete, practical walkthrough of the Filter Schedule Create Scheduled n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:
   Automated Churn Prevention with n8n: AI-Driven Win-Back Campaigns Using Google Sheets and Gemini
   
   Meta Description:
   Explore how a no-code automation in n8n uses Google Sheets and Google Gemini AI to identify high-risk churn customers and send personalized win-back offers daily. Learn how AI, automation, and data can reclaim customer loyalty efficiently.
   
   Keywords:
   n8n, churn prediction, customer retention, automation, Google Sheets, Google Gemini, win-back campaign, AI personalization, low-code marketing, customer engagement, no-code workflow, LangChain, Gmail automation
   
   —
   
   Article:
   
   How to Automate Personalized Win-Back Offers for Churning Customers Using n8n, Google Gemini & Google Sheets
   
   In today’s competitive ecommerce and digital services landscape, retaining customers is just as important as acquiring them. Yet identifying which customers are about to churn — and offering compelling incentives to keep them — can be an enormous manual effort. Thankfully, AI and automation tools like n8n can radically reduce this complexity, making personalized retention campaigns easy and scalable.
   
   This article explores a fully automated n8n workflow that identifies customers likely to churn, generates personalized win-back offers using Google’s Gemini AI model, and delivers these offers via Gmail — all orchestrated with zero code using Google Sheets as the data layer.
   
   Let’s walk through the actual implementation powering this intelligent campaign.
   
   🎯 Goal:
   Automatically identify high-risk churn customers from Google Sheets daily, create AI-generated personalized offers based on their preferences, and send those offers via email.
   
   🔁 Daily Workflow Overview
   
   The automation begins with a scheduled daily trigger in n8n, running once every 24 hours. From this point, the following steps are executed:
   
   Step 1: Fetch Customer Data
   - Source: Google Sheets ("Customer Data" tab)
   - Action: Grab all current customer data containing fields such as predicted churn score, email address, preferred product categories, and last purchase data.
   
   Step 2: Filter for At-Risk Customers
   - Condition: Customers with a predicted_churn_score > 0.7 and who haven’t received a previous campaign (validated against the “created_campaign_date” field).
   - Output: A filtered list of customers segmented by churn risk level, ready for targeted win-back strategies.
   
   Step 3: Branch Based on Eligibility
   - Combinator logic checks whether any eligible customers were found.
   - If none are found, the workflow logs a “NOT_FOUND” entry in a separate “SYSTEM_LOG” Google Sheet.
   - If eligible customers exist, the system loops through them one by one.
   
   Step 4: Personalized Offer Generation via Gemini AI
   This is where the workflow shines. For each at-risk customer, the system calls Google Gemini Pro (via LangChain) to generate a smart offer. The AI uses:
   - Predicted churn score
   - Preferred shopping categories
   
   Based on these parameters, it develops one of three types of offers dynamically:
   1. INFORMATIONAL (Score: 0.7–0.8)
      Example: “Exciting new novels just arrived in your favorite Kitap category!”
   
   2. BONUS_POINTS (Score: 0.81–0.9)
      Example: “Earn 200 bonus points on your next purchase in the Books category!”
   
   3. DISCOUNT_PERCENTAGE (Score: 0.91–1.0)
      Example: “Enjoy 20% off your next Books purchase!”
   
   The offer is returned as structured JSON, parsed using LangChain’s output parser to ensure consistency.
   
   Step 5: Logging and Audit Trail
   Every win-back offer sent is recorded in the "SYSTEM_LOG" Google Sheet with:
   - Timestamp
   - Customer ID
   - Action taken (SENT_WINBACK_OFFER)
   
   This creates a transparent changelog for marketing or operations teams.
   
   Step 6: Send the Win-Back Offers via Gmail
   Using Gmail integration, the workflow sends a personalized email offer to each customer who met the criteria. Customers get a natural, tailored message directly in their inbox — all without manual work.
   
   🔎 Fallback Logging
   If no customers meet the selection criteria during a run, the system automatically logs a “NOT_FOUND” event. This ensures the workflow is self-documenting—no silent failures.
   
   🧠 Why Use AI?
   Traditional marketing campaigns often rely on bulk messages, but AI-driven personalization achieves:
   - Higher engagement rates
   - Campaign efficiency
   - Better ROI on loyalty initiatives
   
   This n8n workflow turns historical customer behavioral data into an effective retention engine, running on autopilot.
   
   📍 Summary & Benefits
   
   ✅ Automated daily churn checks  
   ✅ AI-personalized offers using Google Gemini Pro  
   ✅ Seamless Gmail integration to deliver emails  
   ✅ Transparent logging with Google Sheets  
   ✅ All achieved without writing a line of code  
   
   👉 Whether you're a marketer, product manager, or growth hacker, this setup can revolutionize your customer retention strategy with minimal setup.
   
   —
   
   Third-Party APIs & Services Used:
   
   1. Google Sheets API (via n8n Google Sheets Node)
      - For fetching and logging customer & system data
   2. Gmail API (via n8n Gmail Node)
      - For sending win-back offer emails to customers
   3. Google Gemini Pro (via LangChain using GooglePalm API)
      - For generating intelligent and personalized retention messages
   4. LangChain OutputParser & LLM Chain
      - For structured prompt-response handling and formatting AI output
   
   —
   
   Want to reduce churn and increase customer loyalty with no-code automation? n8n, Google Gemini, and a simple Google Sheet might be all you need.
   
   Let AI do the thinking – and n8n do the sending.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.