This article provides a complete, practical walkthrough of the Splitout Schedule Automation 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:
   Automate Shopify UTM Tracking with n8n and Baserow: A No-Code Workflow Guide
   
   Meta Description:
   Learn how to automate UTM parameter tracking from Shopify orders using an n8n workflow integrated with Baserow. Capture campaign data, analyze marketing performance, and streamline your e-commerce analytics—no code required.
   
   Keywords:
   n8n, Shopify API, Shopify GraphQL, Baserow, UTM tracking, e-commerce analytics, marketing automation, no-code tools, Shopify marketing, campaign tracking, Shopify customer journey, Shopify UTM parameters
   
   Third-Party APIs Used:
   - Shopify GraphQL Admin API
   - Baserow API
   
   Article:
   
   Automate Shopify UTM Tracking with n8n and Baserow: A No-Code Workflow Guide
   
   E-commerce businesses rely heavily on marketing attribution and UTM parameters to monitor the performance of their campaigns. Knowing how and where customers found your brand can guide smarter advertising strategies. However, manually extracting and compiling UTM data from Shopify orders is tedious and error-prone. That's where automation with powerful no-code tools like n8n and Baserow comes in.
   
   In this article, we walk through a fully automated n8n workflow that extracts UTM parameters from Shopify orders via the GraphQL Admin API and logs this data into Baserow for structured analysis — all without writing a line of code.
   
   Overview of the Workflow
   
   This n8n workflow is designed to run daily at midnight. Here's what it does:
   
   1. Triggers every 24 hours.
   2. Sets and uses your Shopify subdomain to access the Shopify store's GraphQL Admin API.
   3. Pulls yesterday’s orders from Shopify.
   4. Parses out relevant UTM parameters from each order's customer journey.
   5. Filters out any orders that don't have UTM campaign data.
   6. Sends the cleaned and enriched data to Baserow for analysis and storage.
   
   Let’s break down each part of the workflow.
   
   1. Scheduled Daily Trigger
   
   The “Every day at 00:00” node is a simple schedule trigger that kicks off the workflow daily at midnight. It ensures that only the previous day's orders are fetched for processing.
   
   2. Defining Your Shopify Domain
   
   The “Set Shopify Subdomain” node allows you to hardcode your Shopify store's subdomain — vital for constructing the correct API endpoint needed later in the workflow.
   
   3. Fetching Orders via Shopify GraphQL
   
   Instead of using a typical REST API call, this workflow takes advantage of Shopify’s more advanced GraphQL Admin API. A custom GraphQL query retrieves all orders created on the previous day, including:
   
   - Order ID and name
   - Total revenue from the order
   - Customer journey summary, especially the first visit UTM parameters: campaign, source, medium, content, and term
   
   This is crucial because the default n8n Shopify node does not offer access to the customer journey metadata — hence the need to use a custom GraphQL call.
   
   4. Handling and Transforming Data
   
   Once the data is retrieved, a "Split Out" node breaks the order list into individual n8n items, so each order can be processed separately. The "Transform incoming data structure" node then restructures the JSON response. It extracts specific fields and applies fallbacks for empty values to avoid flow errors down the line.
   
   5. Filtering for Campaign Data
   
   The “Check if 'Campaign' is present” IF node ensures only orders with marketing attribution data are considered for further processing. This helps keep the destination database clean and focused for marketing analysis, eliminating noise from generic or direct traffic.
   
   6. Logging Data in Baserow
   
   If the campaign condition is met, the data is sent to Baserow via its API. Each relevant UTM parameter — campaign, content, medium, source, term — along with the order ID and revenue, is mapped to a corresponding field in a Baserow table. This allows teams to perform real-time marketing analytics, such as identifying top-performing campaigns or channels.
   
   If the campaign field is absent or null, the workflow branches to a "No Operation" node—a logical endpoint that just does nothing—ensuring the workflow continues smoothly without error.
   
   Why Use n8n and Baserow?
   
   This combination of tools offers excellent flexibility and customization opportunities:
   
   - n8n: As a visual workflow automation tool, n8n excels at connecting APIs and orchestrating data flows using minimal code.
   - Baserow: A powerful no-code database platform, ideal for storing structured data like UTM parameters and supporting custom dashboards or integrations.
   
   Key Benefits
   
   - Fully automated, hands-off system
   - Accurate tracking of marketing campaign data
   - Enhanced attribution analysis by tying UTM data to revenue
   - No need for complicated custom Shopify apps or data warehouse configurations
   - Easily scalable and modifiable to accommodate more data sources, such as Google Ads or Facebook Pixel data
   
   Final Thoughts
   
   This workflow represents the best of modern no-code automation: pulling structured event data from Shopify using GraphQL, processing it logically with n8n, and exporting it to a flexible, user-friendly Baserow database. It saves countless hours of manual tracking and equips marketing teams with the insights they need to drive conversions.
   
   Whether you’re a solo store owner or a growth marketer at a fast-scaling DTC brand, this workflow is an invaluable addition to your analytics stack.
   
   Get started today with n8n and Baserow — and turn your data into decisions.

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.