Description
Overview
The Umami analytics template is a comprehensive automation workflow designed to retrieve, analyze, and archive website analytics data. This orchestration pipeline integrates Umami’s API data with AI-powered analysis, delivering structured SEO insights from raw visitor metrics and page-level statistics.
Intended for digital analysts and SEO professionals, the workflow addresses the challenge of manual data extraction and interpretation by providing a deterministic process that fetches weekly aggregated statistics via a schedule trigger node and generates actionable summaries through AI.
Key Benefits
- Automates weekly retrieval of key website metrics including pageviews, visits, and bounce rates.
- Transforms raw analytics data into structured SEO insights using AI-driven analysis workflow.
- Supports both manual and scheduled triggers for flexible integration into existing operations.
- Stores AI-generated summaries and comparative reports in a centralized Baserow database for traceability.
Product Overview
This automation workflow initiates either manually via a manual trigger node or automatically on a weekly schedule every Thursday. It begins by sending authenticated HTTP requests to the Umami analytics API to fetch detailed website statistics such as pageviews, visitors, visits, bounces, and total time aggregated hourly over the past seven days and adjusted to the Asia/Hong_Kong timezone.
Following data retrieval, JavaScript code nodes parse and simplify the raw JSON response into a structured format containing current and previous period metrics. This formatted data is URL-encoded for safe transmission to an AI service via a secured HTTP POST request using header authentication. The AI, prompted as an SEO expert, generates a markdown summary table of the analytics data.
Further HTTP requests retrieve page-level metrics for the current and preceding week, parsed similarly and sent to the AI for comparative analysis. The AI output includes a markdown table and five practical SEO improvement suggestions. Finally, the workflow saves these AI-generated insights into a Baserow database table with fields for date, summary, top pages, and blog name, enabling systematic record-keeping and reporting.
Error handling relies on the platform’s default retry and failure policies. Authentication is managed using HTTP header credentials for both Umami and Openrouter API calls. No data persistence occurs outside the final Baserow storage.
Features and Outcomes
Core Automation
This orchestration pipeline processes website analytics data by fetching, parsing, and analyzing metrics deterministically. Inputs include time-bounded Umami API data, and decision logic is embedded in JavaScript code nodes that simplify data structure prior to AI consumption.
- Single-pass data transformation ensures consistent metric extraction for analysis.
- Deterministic scheduling triggers weekly execution without manual intervention.
- Explicit separation of summary and page-level data parsing enables modular processing.
Integrations and Intake
The workflow integrates Umami’s website analytics API and Openrouter’s AI chat completion API using HTTP header authentication. It accepts scheduled and manual triggers and requires valid credentials for each external service.
- Umami API provides aggregated and page-level website metrics for multiple timeframes.
- Openrouter API delivers AI-powered SEO analysis based on encoded analytics data.
- Baserow API stores final processed insights in a structured database table.
Outputs and Consumption
Outputs include AI-generated markdown tables summarizing analytics and comparative SEO recommendations. These results are saved asynchronously into Baserow as long text fields, enabling retrieval and further reporting.
- Summary metrics and comparative data presented as markdown tables in AI responses.
- Structured storage in Baserow with date, summary, top pages, and blog name fields.
- Asynchronous response handling supports batch processing of weekly analytics data.
Workflow — End-to-End Execution
Step 1: Trigger
The workflow is initiated either manually via the “When clicking ‘Test workflow’” node or automatically every Thursday by the “Schedule Trigger” node configured for weekly execution. This dual trigger mechanism allows flexible initiation based on operational needs.
Step 2: Processing
Once triggered, an HTTP request node queries Umami’s API to retrieve website analytics for the past seven days. The raw JSON response undergoes parsing in a JavaScript code node, which extracts and encodes key metrics such as pageviews, visitors, bounces, and total time. This ensures consistent, simplified data formatting for downstream AI analysis.
Step 3: Analysis
The processed summary data is sent to Openrouter’s AI via an HTTP POST request with header authentication. The AI model, instructed as an SEO expert, returns a markdown table summarizing the website’s weekly metrics. Additional nodes fetch page-level stats for the current and previous week, parsed and forwarded to AI for comparative analysis and SEO improvement suggestions.
Step 4: Delivery
AI-generated summaries and comparative analyses are stored in Baserow using its API. The data is saved into predefined table fields including date, SEO summary, top pages, and blog name. This persistent storage supports auditability and easy retrieval of historical insights.
Use Cases
Scenario 1
A digital marketing analyst needs to monitor weekly website performance without manually exporting data. This workflow automates data retrieval and analysis, producing structured SEO summaries every week and saving them for review, reducing manual reporting effort.
Scenario 2
An SEO consultant requires comparative insights between current and previous week page visits. The workflow fetches detailed page-level metrics, uses AI to generate a comparative report with improvement suggestions, and stores results for client presentation.
Scenario 3
A website owner wants to integrate analytics insights directly into their project management tools. This orchestration pipeline automatically sends processed SEO data to a Baserow database, enabling seamless integration into existing workflows for ongoing content optimization.
How to use
To deploy this automation workflow in n8n, import the provided workflow JSON and configure the credentials for Umami API and Openrouter AI with HTTP header authentication. Update the Umami website ID and Baserow table/field IDs according to your environment. Activate either the manual trigger for ad-hoc runs or schedule it for weekly execution.
Upon execution, expect the workflow to retrieve your website’s summary and page-level analytics, send them to AI for SEO analysis, and save the generated insights into Baserow. Monitor the workflow executions in n8n to validate successful completions and review stored data within Baserow for actionable SEO recommendations.
Comparison — Manual Process vs. Automation Workflow
| Attribute | Manual/Alternative | This Workflow |
|---|---|---|
| Steps required | Multiple manual exports, data parsing, and analysis in separate tools | Automated data fetching, parsing, AI analysis, and storage in a single pipeline |
| Consistency | Subject to human error and inconsistent timing | Deterministic weekly execution with standardized data processing |
| Scalability | Limited by manual capacity and tool integration complexity | Scales with n8n’s infrastructure and API rate limits without additional effort |
| Maintenance | High, requiring manual updates to extraction and analysis scripts | Low, centralized workflow with credential updates and minimal configuration changes |
Technical Specifications
| Environment | n8n workflow automation platform |
|---|---|
| Tools / APIs | Umami Analytics API, Openrouter AI API, Baserow API |
| Execution Model | Event-driven with manual and scheduled triggers |
| Input Formats | HTTP JSON responses from Umami API |
| Output Formats | Markdown tables in AI responses, JSON for Baserow storage |
| Data Handling | Transient processing, URL-encoded JSON for AI input |
| Known Constraints | Relies on availability of Umami and Openrouter APIs |
| Credentials | HTTP header authentication for Umami API and Openrouter AI |
Implementation Requirements
- Valid HTTP header authentication credentials for Umami API and Openrouter AI API.
- Configured Baserow account with an existing table containing required fields for data storage.
- Properly set Umami website ID and API URLs adjusted for your domain and timezone.
Configuration & Validation
- Confirm API credentials are correctly set and authorized for both Umami and Openrouter nodes.
- Validate HTTP request URLs contain the correct website ID and timezone parameters.
- Run the workflow manually first to verify data retrieval, AI response formatting, and Baserow storage operations.
Data Provenance
- Trigger nodes: “When clicking ‘Test workflow’” (manual), “Schedule Trigger” (weekly).
- Data retrieval nodes: “Get view stats from Umami,” “Get page data from Umami,” and “Get page view data from Umami” using HTTP header authentication.
- AI analysis nodes: “Send data to A.I.” and “Send data to A.I.1” leveraging Openrouter API with header authentication; output saved by “Save data to Baserow.”
FAQ
How is the Umami analytics template automation workflow triggered?
The workflow can be triggered manually through a manual trigger node or automatically on a fixed weekly schedule every Thursday using the schedule trigger node.
Which tools or models does the orchestration pipeline use?
The workflow integrates the Umami Analytics API for data intake and leverages Openrouter’s AI chat completion API with a specific instruct model for SEO data analysis and recommendations.
What does the response look like for client consumption?
AI responses are returned as markdown-formatted tables summarizing website metrics and comparative SEO suggestions, which are then stored as long text entries in a Baserow database for structured review.
Is any data persisted by the workflow?
Data is transiently processed within the workflow and only persisted when saved into the Baserow database table configured for storing analysis summaries and recommendations.
How are errors handled in this integration flow?
Error handling relies on n8n’s default retry and failure mechanisms; no explicit custom error handling or backoff strategies are configured within this workflow.
Conclusion
The Umami analytics template workflow automates the end-to-end process of extracting website analytics, conducting AI-driven SEO analysis, and archiving insights in a structured database. It delivers dependable, scheduled retrieval and comparative reporting of key performance metrics through a no-code integration pipeline. While it requires ongoing API availability and valid credentials for external services, it reduces manual intervention and standardizes analytics reporting through its deterministic design. This establishes a foundation for continuous SEO monitoring and data-driven decision-making in digital marketing environments.
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