Custom Metrics via Ticket Fields – Available ApS

In a context where customer service increasingly functions as a strategic differentiator, standard reporting in Zendesk is often not sufficient. Built-in metrics such as First reply time and Customer Satisfaction (CSAT) are central, but they do not always reflect the business context that defines success. Custom metrics via ticket fields can be used as a mechanism for expanding Zendesk from a support tool into a platform for business intelligence.

What Are Custom Metrics, and Why Are They Crucial?

Custom metrics are user-defined calculations that are built by making use of data collected via tailored ticket fields. They make it possible to quantify, analyse and report on specific KPIs (Key Performance Indicators) that are critical to the business, but that Zendesk's standard metrics do not cover.

They are built on four basic principles:

Custom ticket fields: Data that is unique to the business, such as order value, product line, customer segment or region.
Combined logic: Combining several fields to create nuanced insight, e.g. the resolution time for "high-priority" tickets from "VIP" customers.
Calculated values: The use of mathematical operations such as sum, average, ratio and percentage to create meaningful values.
Business rules: Implementing business logic directly in the reporting, e.g. the definition of a "handled ticket" in a complex workflow.

The Limitations of Standard Metrics

Standard metrics are designed to be general, while business needs are often specific. There may be a need to measure:

Business-specific KPIs: How does support affect sales? What is the average order value for the tickets that are handled? Which products generate the most enquiries?
Custom workflows: Internal processes can be unique, and there may be a need to measure the efficiency of specific workflows, e.g. the time to approve a refund.
Integration data: Zendesk is often used together with other systems, and support data can be enriched with information from CRM, ERP or a webshop.
Strategic business intelligence: Management may need answers to business-critical questions beyond support-operational ones. Custom metrics can act as a bridge between support data and strategic decision-making.

The Building Blocks: Custom Ticket Fields

Before custom metrics can be built, there must be data to calculate on. This data is collected via custom ticket fields. The choice of field type has a bearing on data quality and later reporting options.

Dropdown and multi-select fields

These fields support consistent data by requiring a selection from a predefined list, which reduces spelling mistakes and variations.

Use: Categorising tickets by product type, reason for the enquiry, customer segment (e.g. Enterprise, SMB, Private) or priority.
Best practice: Keep the options mutually exclusive and comprehensive. Avoid "Other" if possible.

Numeric fields

Numeric fields are necessary when calculations are to be carried out.

Use: Collecting order value, the number of returned items, time spent in minutes, or a satisfaction score from 1-10.
Best practice: Specify a clear unit (e.g. DKK, count, minutes) in the field name to avoid doubt.

Date and time fields

Time-based analyses require precise data points.

Use: Recording the expected delivery date, the start date of a subscription, or the deadline for a task.
Best practice: Be aware of time zones, especially when working internationally.

Checkbox fields

Checkbox fields are suited to simple yes/no scenarios.

Use: Marking VIP customers, indicating whether a ticket is related to a legal matter, or whether a customer has accepted an offer.
Best practice: Used to create clear, binary segments that can be used in filters and calculations.

Step by Step: How to Create Custom Metrics in Explore

A valuable custom metric requires planning and precision.

Step 1: Define the goal and the business question

Start with the purpose: which business question is to be answered?

Bad example: "A metric for order value is wanted."
Good example: "Insight is wanted into whether the support team has a positive impact on customer loyalty by comparing the order value before and after a support interaction."

Step 2: Design and configure fields

Based on the goal, the necessary custom ticket fields are designed. Reporting should be thought into the design from the start: are dropdowns better than text fields? Should a field be mandatory? The fields must be added to the relevant ticket forms, so that they can be filled in by agents.

Step 3: Build the metric in Zendesk Explore

Navigate to Zendesk Explore, and create a new query based on the relevant dataset (typically Support: Tickets).
In the Calculations (metrics) panel, select Add standard calculated metric.
Give it a descriptive name, e.g. "AVG Order Value per Solved Ticket".
Enter the formula in the formula field. A typical formula consists of an aggregator (SUM, COUNT, AVG), a field (e.g. Ticket field value) and often filters.

Example formula:
IF ([Ticket status] = "Solved") THEN [Ticket field - Ordreværdi] ENDIF
The formula ensures that the order value is only counted for solved tickets.

Step 4: Test and validate

A metric only has value if it is correct.

Manual test: Select a small number of tickets, and calculate the value manually. Does the result match the metric?
Edge cases: The handling of empty fields must be correct, including NULL values.
Correlogram: Place the metric side by side with related standard metrics to assess whether the development is plausible.

Advanced Examples of Custom Metrics

The examples below illustrate the use of custom metrics.

1. Revenue per ticket type

Purpose: To understand which types of enquiry generate the most revenue.
Required fields: Dropdown "Ticket Type" (e.g. Sales, Support, Question), numeric "Order Value".
Formula in Explore:
```
IF ([Ticket field - Ticket type] = "Salg") THEN [Ticket field - Ordreværdi] ENDIF
```
The metric can then be grouped by "Ticket type" to create an overview.

2. Resolution rate per product

Purpose: To identify products with a low or high resolution rate, which can indicate challenges with documentation or product quality.
Required fields: Dropdown "Produkt".

Formula in Explore:

// First create a metric for solved tickets per product
IF ([Ticket status] = "Solved") THEN [Ticket ID] ENDIF

// Then create a metric for total tickets per product
[Ticket ID]

// Then use "Result manipulation" in the query to calculate the ratio:
// COUNT(Solved tickets) / COUNT(Total tickets)

3. First contact resolution (FCR) per customer segment

Purpose: To measure the ability to solve the customer's problem in the first interaction, segmented by the customer's value.
Required fields: Dropdown "Kundesegment" (VIP, Standard), checkbox "Løst i Første Henvendelse" (filled in by the agent).
Formula in Explore:
```
IF ([Ticket field - Løst i Første Henvendelse] = TRUE) THEN [Ticket ID] ENDIF
```
The metric is used as the numerator and COUNT(Tickets) as the denominator to calculate the percentage. Group by "Kundesegment".

Best Practices for Sustainable and Value-Creating Reporting

Planning is the key: Before creating fields, a "data dictionary" should be prepared. Document each field's purpose, data type and possible values. This supports consistency and makes onboarding easier.
Ensure data quality: Data quality is crucial. Make fields mandatory where necessary. Use dropdowns to limit free text. Train agents in correct data entry and explain the significance.
Start simple and iterate: Avoid building a complex metric from the start. Begin with a simple version, test it, and then build it out. This makes validation and adjustment easier.
Make insights actionable: A report is a tool, and the value is created through action. When sharing dashboards, an analysis should be included: "What do the numbers mean, and which actions should be taken to improve them?"

Integration with External Systems: The True 360-Degree View

The greatest effect is achieved by combining Zendesk data with data from other systems. Via webhooks, APIs or native integrations (e.g. Salesforce), data can be loaded into custom ticket fields.

CRM data: Load the customer's lifetime value (LTV) or contract size into Zendesk. This makes it possible to analyse whether proportionally more time is spent on the most valuable customers.
ERP data: Import order information such as order number or shipping status directly onto the ticket. This makes metrics such as "Average time from order to support enquiry" possible.
Financial data: By connecting support costs (agent time) with order value, "Support Cost of Revenue" can be calculated, and the profitability of the support department can be measured.

Troubleshooting and Challenges

Even with good planning, challenges can arise.

Problem: The metric shows null or incorrect data.
- Solution: Check that the field is active on the ticket form used. Verify that the field is filled in on the tickets being analysed. Review the formula for syntax errors, especially IF/THEN/ENDIF logic and the handling of empty fields.
Problem: Data is inconsistent (e.g. "København", "KBH", "Cph" for the same city).
- Solution: Inconsistency is often due to free-text fields. Convert the field to a dropdown field. Existing data can be standardised via "Bulk Update" in Zendesk. Consider using "Business Rules" (automations and macros) to fill in automatically based on other criteria.
Problem: Dashboards are slow to load.
- Solution: Very complex calculated metrics can affect performance in Explore. Assess whether the formula can be simplified. In some cases, it is better to build the logic into several simpler metrics and combine them in the query rather than one large, complex formula.

Conclusion: From Data to Strategic Value

Custom metrics via ticket fields are both a technical and a strategic discipline. By designing data collection carefully and building relevant calculations, Zendesk can be used for proactive business insight. It supports documentation of the support department's value, optimisation of processes based on data, and better decisions that can strengthen both customer satisfaction and the bottom line. This is where raw data is converted into strategic value.