This article provides a complete, practical walkthrough of the Googleanalytics Code Automation Webhook 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:**
   Automate SEO Insights with n8n: Comparing Weekly Google Analytics Data Using AI
   
   **Meta Description:**
   Discover how to automate your SEO reporting using n8n, Google Analytics, and AI. Learn how a no-code workflow collects and compares metrics weekly, extracts insights via LLMs, and stores results in Baserow.
   
   **Keywords:**
   n8n workflow, SEO automation, Google Analytics GA4, AI-powered reporting, Baserow integration, OpenRouter AI, no-code SEO tools, GA4 data comparison, website analytics automation, AI SEO expert
   
   ---
   
   # Automate SEO Insights with n8n: Comparing Weekly Google Analytics Data Using AI
   
   In the age of AI-assisted productivity, automating your website analytics can unlock valuable SEO insights with minimal effort. Using n8n, an open-source workflow automation platform, you can build a no-code system that collects data from Google Analytics (GA4), passes it through a language model for analysis, and logs the results into a database for seamless reporting.
   
   This article explores a specific n8n workflow that collects weekly website engagement metrics, search performance data, and geographic user distribution. It then compares the latest data with the previous week's data using an AI model and stores the results in Baserow, a no-code database platform. By using the power of automation and AI, this workflow enables consistent SEO monitoring and improvement without the need for manual reports.
   
   ## The Purpose of the Workflow
   
   The primary goal of this workflow, titled “Google Analytics Template,” is to:
   
   - Collect weekly analytics data from Google Analytics 4
   - Compare current and previous metrics (weekly)
   - Leverage AI (via OpenRouter) to analyze trends, generate markdown summaries, and suggest actionable SEO improvements
   - Store those summaries and suggestions into Baserow for tracking and reference
   
   This process creates a low-maintenance SEO reporting tool for digital marketers, bloggers, and SEO professionals.
   
   ## How the Workflow is Structured
   
   Let’s break down how the workflow functions and its key stages:
   
   ### 1. Triggering the Workflow
   The workflow can be manually triggered or scheduled to run weekly using the built-in Schedule Trigger node in n8n. This activates the process for collecting the latest data from various Google Analytics reports.
   
   ### 2. Data Collection from Google Analytics
   There are three key types of data pulled:
   
   - **Page Engagement Metrics:** Metrics like active users, screen page views, session events, and views per user, grouped by page URL.
   - **Search Console Data from Google Analytics 4:** Includes search engagement metrics such as click-through rates, average position in Google results, and impressions.
   - **Geographic Engagement Data:** Active users, sessions, engagement rates, and new users by country.
   
   Each dataset is collected in two time frames: the current week and the previous week. This is done using GA4’s custom date filtering in API calls with nodes like:
   
   - `Get Page Engagement Stats for this week`
   - `Get Page Engagement Stats for prior week`
   - `Get Google Search Results for this week`
   - `Get Google Search Results for last week`
   - `Get Country views data for this week`
   - `Get Country views data for last week`
   
   ### 3. Parsing and Formatting the Data
   The raw GA4 data is structured and transformed with JavaScript code nodes that format the data into simplified JSON arrays. These arrays are URL-encoded strings that can be safely injected into the AI prompts.
   
   Examples of custom code nodes include:
   
   - `Parse data from Google Analytics`
   - `Parse Google Analytics Data1`
   - `Parse Google analytics data1`
   
   This transformation ensures the LLM receives clean structured data text for comparison.
   
   ### 4. Sending Data to the LLM via OpenRouter API
   The workflow then sends the URL-encoded data to OpenRouter—a routing API that grants access to various large language models, including Meta’s Llama-3.1-70B-instruct.
   
   There are three separate AI prompt executions for each dataset:
   
   - **Page Data Analysis**
   - **Search Console Analysis**
   - **Country-Based User Analysis**
   
   Each request contains a prompt instructing the AI to act as an SEO expert. The AI compares both weeks’ data, outputs a markdown-formatted comparison table, and provides five recommendations for SEO improvement.
   
   These nodes include:
   
   - `Send page data to A.I.`
   - `Send page Search data to A.I.`
   - `Send country view data to A.I.`
   
   ### 5. Saving AI Output in Baserow
   Finally, the AI’s output is saved into Baserow, a no-code relational database, for archiving, dashboarding, or further analysis. The `Save A.I. output to Baserow` node maps the markdown content of each comparison (page, search, and geographic data) along with the website name and timestamp into a Baserow table.
   
   The required Baserow table structure includes fields like:
   
   - Blog (website name)
   - Page Views
   - Country Views
   - Search Report
   - Date
   
   ## Benefits of This Workflow
   
   Here’s what makes this setup exceptionally useful:
   
   - 🔄 Fully automated: Once configured and scheduled, it runs without any manual intervention.
   - 📊 Comparative insights: By contrasting metrics week-over-week, it helps identify what’s improving or underperforming.
   - 🧠 AI-powered recommendations: The use of language models provides human-like insights into actionable SEO upgrades.
   - 🗃️ Unified data storage: Results are saved in Baserow for future lookup, reporting, or visualization.
   
   ## Third-Party APIs Used
   
   This workflow uses the following external services:
   
   1. **Google Analytics (GA4) API** – For collecting user, engagement, and geographic performance data.
   2. **OpenRouter API** – To access LLMs (specifically Meta’s Llama 3.1 70B) for interpreting the data and generating SEO-focused reports.
   3. **Baserow API** – For persisting AI insights and allowing future organization and visualization.
   
   ## Getting Started
   
   To replicate this workflow, you’ll need:
   
   - A GA4 property and credentials set up in n8n
   - A Baserow table with proper columns
   - An API key from OpenRouter with access to the Llama model
   - Header Authentication properly configured in your n8n credentials (e.g., “Bearer YOUR_API_KEY”)
   
   This automation is highly customizable—you can modify prompts, substitute models, or enrich the workflow with other KPIs.
   
   ## Final Thoughts
   
   This n8n-based automation bridges the gap between raw analytics and strategic SEO decisions. It leverages structured data, AI, and visual storage seamlessly, saving time and enhancing understanding. Whether you manage a single blog or multiple client websites, automating this analysis ensures you always stay a step ahead in your SEO game.
   
   🔗 [Read the full walkthrough and tutorial here.](https://rumjahn.com/how-i-used-a-i-to-be-an-seo-expert-and-analyzed-my-google-analytics-data-in-n8n-and-make-com/)
   
   —
   
   Want to dive deeper? Explore, customize, and build on this n8n template to reinvent how you manage your digital content's performance.
   
   --- 
   
   Let me know if you'd like the article to be formatted as HTML or exported for Markdown editors like Obsidian or Notion!

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.