## Introduction
For data and analytics teams in startups and growing organizations, accurately tracking time-to-resolution (TTR) metrics is critical for understanding operational efficiency, customer support performance, and overall business health. Manual tracking of TTR data is error-prone, time-consuming, and usually outdated by the time insights are generated. Automating TTR measurement using a workflow automation tool like n8n not only reduces manual work but also ensures real-time, consistent, and actionable metrics.
In this article, we will build a detailed step-by-step automation workflow using **n8n** to track time-to-resolution for support tickets or any task lifecycle managed in tools such as Zendesk, Jira, or GitHub Issues. This approach suits data teams aiming to integrate TTR data into their analytics pipelines efficiently.
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## Problem Statement and Benefits
**Problem:**
– Manual calculation of time-to-resolution leads to errors and delays.
– Lack of real-time visibility into ticket or issue resolution efficiency.
– Disparate systems make centralized measurement and reporting difficult.
**Who Benefits:**
– Data & Analytics teams gain accurate, up-to-date metrics.
– Customer support and operations managers get actionable insights.
– Automation engineers streamline reporting with less manual intervention.
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## Tools and Services Integrated
– **n8n:** The open-source workflow automation tool to orchestrate data fetching, processing, and updating.
– **Support Ticket or Issue Management System:** For this tutorial, we use **Zendesk** as an example, but Jira, GitHub Issues, or others can be adapted similarly.
– **Google Sheets:** For storing and tracking the processed TTR metrics. Alternatively, a database or BI tool integration is possible.
– **Slack:** Optional, for alerting when thresholds (e.g., resolution delays) are exceeded.
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## Workflow Overview
1. **Trigger:** Scheduled trigger runs the workflow every hour/day.
2. **Fetch Tickets:** Pull newly resolved tickets from Zendesk API.
3. **Calculate Time-to-Resolution:** Compute the difference between ticket creation and resolution timestamps.
4. **Store Metrics:** Append or update the ticket TTR data in Google Sheets.
5. **Alerting (Optional):** Send Slack notifications if resolution time exceeds SLA.
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## Step-by-Step Workflow Tutorial
### Step 1: Setup n8n and Required Credentials
– Install n8n (locally or on a server).
– Configure credentials for Zendesk API:
– Obtain your Zendesk subdomain, email, and API token.
– Configure Google Sheets API credentials:
– Create OAuth credentials from Google Cloud Console.
– (Optional) Setup Slack API webhook or bot token.
### Step 2: Create a Scheduled Trigger Node
– Add a **Cron** node in n8n.
– Set it to run daily/hourly depending on how often you wish to update metrics.
– For example, run at 00:00 every day to capture previous day’s tickets.
### Step 3: Retrieve Resolved Tickets from Zendesk
– Add an **HTTP Request** node.
– Configure it to call Zendesk’s Tickets API endpoint filtered by status = “solved” and updated_at within the last run period.
– Example API URL:
`https://{subdomain}.zendesk.com/api/v2/search.json?query=status:solved updated>{last_run_time}`
– Use n8n expressions or variables to dynamically set the `updated` filter based on the previous execution time.
– Use Basic Auth with your email/token.
### Step 4: Parse Tickets and Calculate Time-to-Resolution
– Add a **Function** node.
– Iterate over each ticket fetched to compute TTR:
“`javascript
return items.map(item => {
const createdAt = new Date(item.json.created_at);
const solvedAt = new Date(item.json.updated_at); // Zendesk uses updated_at for solved timestamp
const timeToResolutionMs = solvedAt – createdAt;
const timeToResolutionHours = timeToResolutionMs / (1000 * 60 * 60);
return {
json: {
ticket_id: item.json.id,
requester_id: item.json.requester_id,
created_at: item.json.created_at,
solved_at: item.json.updated_at,
ttr_hours: timeToResolutionHours
}
};
});
“`
– This calculation assumes resolution time equals the difference between ticket creation and its last update timestamp when status changed to solved.
### Step 5: Update Google Sheets with TTR Data
– Add a **Google Sheets** node configured to append new rows or update existing rows based on ticket ID.
– Map columns:
– Ticket ID
– Requester ID
– Created At
– Solved At
– Time to Resolution (Hours)
– Use the Update Row operation if the ticket exists by matching Ticket ID, otherwise Append Row.
– This ensures an up-to-date sheet with all resolved ticket TTR data.
### Step 6: (Optional) Send Slack Alerts on SLA Violations
– Add a **IF** node to check if `ttr_hours` exceeds your SLA threshold (e.g., 24 hours).
– For tickets exceeding limits, send a message using a **Slack** node:
– Format the alert with ticket details and TTR.
– Post to relevant Slack channel or user.
### Step 7: Finalize and Test Workflow
– Save and activate the workflow.
– Run manually initially to verify:
– Tickets are correctly retrieved.
– Time-to-resolution is calculated accurately.
– Data is stored/updated in Google Sheets.
– Slack alerts fire appropriately.
– Adjust filters and timing as per your business rules.
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## Common Errors and Tips for Robustness
### Common Errors
– **API Rate Limits:** Zendesk and Google APIs have limits; implement retries or rate limiting in n8n nodes.
– **Field Inconsistencies:** Timestamps must be in ISO format; verify ticket fields exist.
– **Time Zone Differences:** Normalize all timestamps to UTC before calculating TTR.
– **Duplicate Entries:** Use logic to avoid double-counting tickets already processed.
### Tips
– Use n8n’s built-in variables to track last successful run timestamp for incremental data loads.
– Add error handling nodes to catch and log API errors.
– Secure credentials using environment variables or n8n’s credential vault.
– Schedule the workflow during off-peak hours to reduce API load.
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## How to Adapt and Scale the Workflow
– **Integrate with Other Ticketing Tools:** Replace Zendesk API calls with Jira or GitHub Issue API calls; adjust fields accordingly.
– **Connect to a Database:** Instead of Google Sheets, push TTR data to a SQL or NoSQL database for advanced querying.
– **Add More Metrics:** Extend workflow to calculate additional KPIs like first response time or backlog aging.
– **Automated Reporting:** Connect the workflow output to BI tools (e.g., Looker, Tableau) through APIs for visual dashboards.
– **Multi-Department Support:** Use conditional nodes to branch workflows and handle different teams or ticket types.
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## Summary and Bonus Tip
Automating time-to-resolution tracking with n8n can drastically improve accuracy, timeliness, and usability of your support metrics. This workflow empowers your data and analytics team to spend more time analyzing and less time wrangling data. The stepwise construction allows easy customization, leveraging low-code automation to streamline operational insights.
**Bonus Tip:** For enhanced automation, combine n8n with a webhook trigger from your ticketing system to capture real-time resolution events instead of scheduled polling, enabling near-instantaneous TTR tracking.
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Implementing this n8n workflow will elevate your organization’s operational visibility and efficiency, setting a solid foundation for continuous improvement in customer experience and internal processes.