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: 
   How I Automated Weekly SEO Insights with Google Analytics, AI, and Baserow Using n8n
   
   Meta Description:
   Learn how to build a no-code automation workflow in n8n that analyzes Google Analytics data, compares it week over week, sends it to AI for SEO insights, and stores the results in Baserow.
   
   Keywords:
   n8n workflow, Google Analytics GA4, OpenRouter AI, Baserow automation, no-code SEO, SEO insights, AI-driven marketing, weekly data report, page performance, country views, search console, n8n automation, AI SEO analysis
   
   List of Third-Party APIs Used:
   
   1. Google Analytics 4 (GA4) API
   2. OpenRouter AI API (via meta-llama/llama-3.1-70b-instruct)
   3. Baserow API
   
   Short Article:
   
   Automating SEO Insights with n8n, Google Analytics, AI, and Baserow
   
   Tracking website performance metrics is crucial for improving content strategy and SEO, but collecting, comparing, and interpreting analytics manually every week is tedious. That’s why I built an intelligent automation in n8n that pulls Google Analytics data, compares weekly stats, generates actionable insights using AI, and stores everything neatly in a Baserow database.
   
   Let’s walk through how this powerful no-code SEO workflow works.
   
   1. Scheduled Data Collection
   
   The workflow begins with a Schedule Trigger in n8n set to run weekly. It also supports a Manual Trigger for on-demand testing.
   
   It collects three major categories of data from Google Analytics GA4 using OAuth2 authentication:
   
   - Page Engagement Stats:
     Metrics like screen page views, active users, and event counts, broken down by page name.
     
   - Google Search Results:
     Metrics such as average position, click-through rate (CTR), organic impressions, and engaged sessions, grouped by landing pages.
     
   - Country Views:
     Engagement and session data segmented by user location (country), giving insight into geographic traffic.
   
   Each data source is queried for the current week and the prior week to establish a comparative baseline.
   
   2. Data Transformation with Custom Code
   
   The raw data returned from Google Analytics isn't usable directly. Each of the six data sets—this week and last week for each category—is processed using custom JavaScript in n8n's Code node.
   
   These functions ensure:
   
   - Only valid and structured data is passed forward.
   - The values are parsed into clean objects.
   - JSON is URL-encoded for safe transmission to an API endpoint (in this case, the AI).
   
   This ensures that even if the structure changes slightly or the dataset becomes inconsistent, the system can log errors clearly and handle failures gracefully.
   
   3. AI-Powered SEO Analysis
   
   Each pair of datasets (this week vs. last week) is sent to OpenRouter’s AI model ‘meta-llama/llama-3.1-70b-instruct’. This large language model is prompted as an SEO expert.
   
   The prompt template is:
   
   "You are an SEO expert. Compare the data from the past two weeks, give me a table in markdown. Then give me 5 suggestions to improve my SEO. Output the data so that it works with markdown editors."
   
   The AI returns:
   
   - A markdown table presenting a side-by-side comparison of the key metrics.
   - Five targeted SEO suggestions based on the data trends.
   
   This smart automation transforms raw analytics data into understandable and practical advice—no analyst required.
   
   4. Storing Results in Baserow
   
   Finally, the AI outputs are saved to a Baserow database. Fields include:
   
   - Blog name.
   - AI-generated insights for Page Views, Search Performance, and Country Views.
   - Timestamp for tracking historical reports.
   
   This step makes the compiled data searchable, structured, and easy to share or integrate into dashboards for stakeholders.
   
   5. Configuration Notes
   
   To make this workflow functional, a few configurations are required:
   
   - Create Google Analytics OAuth2 credentials in n8n and capture your GA4 Property ID.
   - Sign up on openrouter.ai and configure Header authentication with a Bearer token for the AI requests.
   - Create a Baserow database table with specific fields (Name, Country Views, Page Views, Search Report, Blog).
   
   This entire setup is designed with modularity and scalability in mind. New metrics or endpoints can be added by duplicating node patterns.
   
   Conclusion
   
   This automation turns three key data sources from Google Analytics into a weekly SEO report powered by AI—delivered and stored automatically. The process takes minutes to run and provides insights that would otherwise require an analyst's time and SEO expertise.
   
   Whether you're a content creator, marketer, or developer looking to automate insights, this n8n workflow is a low-code blueprint for intelligent automation merging data collection, AI analytics, and structured storage.
   
   You can read more about how this workflow was conceived and built in this article: [“How I used AI to be an SEO expert with n8n and Google Analytics”](https://rumjahn.com/how-i-used-a-i-to-be-an-seo-expert-and-analyzed-my-google-analytics-data-in-n8n-and-make-com/).
   
   Ready to automate your insights? Try it with n8n and see the difference.

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.