Understanding what can actually be reported on in Zendesk Explore is the basis for designing effective dashboards and reports that deliver valuable and action-oriented insight. It is crucial to be able to translate raw data from customer service into strategic knowledge. This guide covers the core of Explore: attributes and metrics, and describes the central concepts for data analysis.
The foundation for data analysis in Zendesk
Zendesk Explore is an analytics tool designed to make data accessible and understandable across the organisation. Explore is more than a reporting tool and can support continuous improvement. With Explore, you have access to:
- Customised dashboards: The ability to design dashboards that reflect the KPIs that matter most to the business – from a management overview to detailed agent metrics.
- Advanced analysis: The ability to analyse data in depth, explore trends, identify bottlenecks and understand underlying causes.
- Real-time data: The ability to make decisions based on up-to-date information, since dashboards are updated on an ongoing basis.
- Custom metrics: The ability to build metrics tailored to specific business goals and processes.
The core of Explore: distinguishing between attributes and metrics
To build meaningful reports, it is essential to understand the difference between attributes and metrics. It can be seen as the difference between "who/what" and "how many/how much".
- Attributes are descriptive, categorical data. They answer questions such as who created the ticket, what the status is, and where the ticket came from. They are used to group, filter and segment data.
- Metrics are measurable, numerical values. They answer questions such as how many tickets have been resolved, how quickly replies are given, and how good customer satisfaction is. They are used to count, calculate and compare.
| Property | Attributes (dimensions) | Metrics (measures) |
|---|---|---|
| Data type | Text, date, boolean (true/false) | Number, percentage, time |
| Purpose | Describe and categorise | Measure and quantify |
| Examples | Ticket ID, Status, Agent, Priority | Number of tickets, Reply time, CSAT score |
| Use in reports | Axes (rows/columns), filters | Values in charts |
Attributes: the descriptive dimensions
Attributes are the building blocks that create context around metrics. Without attributes, a number such as "150" can be meaningless. With attributes such as "Status: Solved" and "Month: June", the number becomes an insight.
Ticket attributes: what, who and where
These are among the most common attributes and describe the ticket itself.
- Ticket ID: The unique identifier for a ticket. A primary key that ensures each individual enquiry can be tracked.
- Subject: The title of the ticket, which provides a quick overview of the enquiry.
- Status: The ticket's current position in the life cycle (New, Open, Pending, Solved, Closed). Central to understanding the workload.
- Priority: The ticket's level of urgency (Urgent, High, Normal, Low). Supports correct prioritisation of resources.
- Type: The category of enquiry (Question, Incident, Problem, Task). Used to analyse different types of work.
- Group: The team or department the ticket is assigned to.
- Assignee: The agent working on the ticket. Essential for performance analysis.
- Requester: The customer or end user who created the ticket.
- Brand: The brand the ticket is associated with, if working with multiple brands.
- Form: The form used to create the ticket. Can provide insight into the intent of the enquiry.
Time attributes: when did it happen?
Time attributes are crucial for trend analysis and understanding processes.
- Created date: The time the ticket is created in the system. The starting point for time-based calculations.
- Updated date: The time of the most recent activity on the ticket.
- Solved date: The time the ticket is marked as solved.
- Closed date: The time the ticket is finally closed, often after the customer's confirmation.
- First reply date: The time of the first reply from an agent. A central factor for the customer experience.
- Last reply date: The time of the most recent reply in a conversation.
Agent and organisation attributes
These attributes provide context about the people and groups involved.
- Agent name, role, group: Information about the assigned agent.
- Organisation name: The company the customer belongs to. Important in a B2B context.
Custom Fields
Custom ticket fields automatically become available as attributes in Explore. This makes it possible to report on data that is specific to the business, e.g. product category, order number or error type.
Metrics: the measurable results
Metrics are the numbers that are typically optimised. They show performance and indicate where there may be potential for improvement.
Volume metrics: how much work?
These metrics provide an overview of the volume of work coming in and out of the system.
- Ticket count: The most basic measure of workload.
- New tickets: The number of new enquiries in a given period.
- Solved tickets: The number of tickets marked as solved.
- Closed tickets: The number of tickets that have been finally closed.
Time metrics: how quickly is the work done?
These metrics measure speed and responsiveness.
- First reply time: The average time before the first reply on a new ticket. A key indicator of customer satisfaction.
- Next reply time: The average time to a reply after the first interaction.
- Resolution time: The average time from creation to resolution.
- Full resolution time: The total time, incl. periods where the ticket has been in "Pending".
SLA metrics: are the agreements being met?
Service Level Agreements (SLAs) describe agreed service levels. These metrics show whether they are being met.
- SLA met: The number of tickets that were resolved within the agreed time frame.
- SLA breached: The number of tickets that exceeded the agreed time frame.
- SLA compliance: The percentage of tickets that met the SLA.
Satisfaction metrics: how satisfied are the customers?
Customer satisfaction (CSAT) is a central indicator of quality.
- Satisfaction score: The average score (e.g. on a scale of 1-5).
- Satisfaction responses: The number of customers who have provided feedback.
- Satisfaction rate: The percentage of "good" (e.g. "Good" or "Excellent") responses relative to the total number of responses.
The power of customisation: creating Custom Metrics
Standard metrics cover many needs, but Explore also supports the creation of your own metrics. Custom metrics make it possible to combine existing data in new ways to answer specific business questions.
Types of Custom Metrics
Two types can primarily be created:
-
Standard Metrics: A simple count or sum of an attribute, e.g.
COUNT(Tickets). - Calculated Metrics: More complex formulas with mathematical operations, conditions and combinations of multiple metrics.
Practical examples of Custom Metrics
Below are examples of calculated metrics that can be used to create deeper insight.
Example 1: Resolution rate for high priority
Measures how efficiently the most pressing cases are handled.
Metric: "High Priority Resolution Rate"
Formula:
IF [Ticket priority] = "High"
THEN [Solved tickets] / [Tickets]
ELSE NULL
ENDIF
Example 2: First Contact Resolution rate (FCR)
Measures the percentage of tickets resolved in the first interaction.
Metric: "First Contact Resolution Rate"
Formula:
IF [Agent replies] = 1
THEN [Solved tickets] / [Tickets]
ELSE NULL
ENDIF
Example 3: Average reply time per channel
Shows whether the reply time varies depending on the channel (email, chat or social media).
Metric: "Avg First Reply Time by Channel"
Formula:
[First reply time (min)] / [Ticket count]
(This metric is then used with "Channel" as a row or column attribute).
Strategic application: Best Practices for effective reporting
Access to data is one thing; strategic application is another. Below are recommendations for effective reporting.
1. Understand the data model
Before building reports, you should understand the relationships in Zendesk data: how tickets are linked to users and organisations, and which attributes are available in which datasets (e.g. Support, Talk, Chat). A solid understanding of the data model reduces errors and saves time.
2. Start simple and build on it
It is recommended to start with basic reports that answer the most fundamental questions:
- How many tickets are received per day?
- What is the average resolution time?
- What does the CSAT score look like?
Once this is in place, more dimensions (attributes) can be added, and metrics can be combined for more nuanced analysis.
3. Choose the right granularity
It is important to choose an appropriate time interval for reports.
- Daily: Suitable for detailed operational analysis and for identifying urgent problems.
- Weekly: Suitable for short-term trends and weekly team meetings.
- Monthly: Suitable for a strategic overview, management reporting and long-term goals.
4. Document the metrics
When creating custom metrics, documentation is important: what the metric measures, how it is calculated, and what the purpose is. This ensures consistent interpretation and reduces the risk of knowledge loss.
The building blocks for valuable dashboards: common report types
Below are classic report types that are often used. Each report type answers a central business question.
1. Volume report
Business question: How much work is received, and how is it distributed?
Metrics: Number of tickets
Dimensions: Status, Priority, Group, Channel
Time period: Daily, Weekly, Monthly
2. Performance report
Business question: How efficient and fast are workflows in terms of resolving the customer's problem?
Metrics: Resolution time, First reply time, Full resolution time
Dimensions: Group, Assignee, Priority
Time period: Weekly, Monthly
3. SLA report
Business question: Are the agreements made with customers being met?
Metrics: SLA met, SLA breached, SLA compliance (%)
Dimensions: Group, Priority, Form
Time period: Weekly, Monthly
4. Satisfaction report
Business question: How satisfied are customers with the service provided?
Metrics: Satisfaction score, Satisfaction rate
Dimensions: Group, Assignee, Ticket type
Time period: Monthly
5. Channel analysis
Business question: Which channels are used, and how does performance vary across them?
Metrics: Number of tickets, First reply time, Satisfaction score
Dimensions: Channel (Email, Chat, Web form, Social)
Time period: Weekly, Monthly
Solving challenges: troubleshooting in Explore
Even experienced users can run into challenges. Below are common problems and their solutions.
Problem: A metric shows incorrect or unexpected data.
- Solution: Double-check the metric definition: is the formula correct? Also check the report's filters, as data may be filtered out unintentionally. In addition, verify that the correct dataset is being used.
Problem: A desired attribute is not available.
- Solution: First check whether the attribute exists in Zendesk Support (e.g. as a custom field). If it exists but does not appear in Explore, there may be a delay in synchronisation. If it is still not available, you should verify that the Explore licence includes the relevant dataset and that the necessary permissions have been granted.
Problem: A report loads very slowly.
- Solution: Report speed depends on complexity and data volume. Reduce the time period for the query, remove unnecessary dimensions or metrics, and consider splitting a complex report into several simpler reports.
Problem: A calculated metric returns an error.
- Solution: Read through the error message, as it often indicates the cause (e.g. "Division by zero" or "Invalid syntax"). Check brackets and data types. A common error is trying to combine text and numbers in the same calculation.
Next steps
Mastering Zendesk Explore is an ongoing process. It is recommended to start with the basics and build up skills over time.
- Explore the library: Review the standard attributes and metrics in the relevant datasets.
- Build your first report: Choose a simple metric such as "Ticket count" and a dimension such as "Status", and observe the changes when adding more dimensions.
- Experiment with Custom Metrics: Create a simple calculated metric, e.g. one that shows the number of "Urgent" tickets.
- Build a focused dashboard: Gather 3-5 key reports that answer the most important questions for the team into one dashboard.
- Share and discuss: Share dashboards with the team and management, and use the data as a starting point for discussions about improvements.
By working systematically with attributes and metrics, Zendesk data can be used as an active tool for continuous improvement and strategic development.