A practical Zendesk Explore reporting tutorial for support managers

If you're a support manager, you know that the answers to improving your team's performance are hiding somewhere in your data. The problem is, actually finding those answers can feel like a full-time job. You've got a tool like Zendesk Explore that promises to unlock everything, but its complexity can be a real roadblock, leaving you and your team staring at basic dashboards that don't tell the full story.

This guide is here to help you cut through that complexity. We'll walk through the essentials of getting started with Zendesk Explore reporting. But more importantly, we’ll look beyond the standard charts and show you a smarter, quicker way to get the kind of insights that actually help improve your customer experience and team efficiency.

What is Zendesk Explore?

Simply put, Zendesk Explore is Zendesk’s own analytics tool. It’s designed to help you measure and make sense of all the activity happening across your Zendesk suite, including Support, Talk, Chat, and Guide.

It’s built around three main ideas:

• Datasets: Think of these as pre-sorted libraries of your information. You’ll have a dataset for tickets, another for agent updates, and so on.

• Reports: These are the individual charts and tables you build to answer a specific question, like "How many tickets did we solve last week?"

• Dashboards: This is where you pull all your reports together to get a high-level view of what’s going on.

It’s a solid tool for digging into what’s happened in the past. But when you need quick answers for what to do next, especially with AI changing the support landscape, you might start to feel its limitations.

A step-by-step Zendesk Explore reporting tutorial

Getting your hands dirty in Zendesk Explore is the best way to learn. Once you get your head around how datasets, metrics, and attributes play together, you can start building reports that actually mean something to your team.

Getting the core concepts: Datasets, metrics, and attributes

Before you build anything, it helps to know what the building blocks are.

• Datasets: This is the library of information you pull from. For most ticket-related questions, you'll use the "Support: Tickets" dataset. If you wanted to see how many times an agent touched a ticket, you'd use "Support: Updates History". Picking the right dataset is the most important first step, and honestly, it’s half the battle.

• Metrics: These are the numbers you want to measure. It’s almost always a number, like the count of solved tickets or the average first reply time.

• Attributes: These are the details you use to slice and dice your metrics. Attributes answer questions like "who," "what," or "when." Think ticket status, assignee name, or ticket creation date.

Creating your first report: Tracking solved tickets by agent

Let's try building a simple but useful report to see how this all comes together.

1. From the Explore reports library, click New report and pick the Support: Tickets dataset.

2. In the Metrics panel on the right, click Add, find Solved tickets, and click Apply. Right away, you'll see a single number: the total count of every ticket ever solved. Not super useful yet.

3. Let's break it down by agent. Click Add in the Columns panel. Search for Assignee name, select it, and hit Apply. Now you have a chart showing who solved what. Much better.

4. Finally, let's make it timely. Click Add in the Filters panel and select Time - Ticket solved. Choose a relevant date range, like "Last 30 days," and you’re good to go.

Even this simple process shows you the mental gymnastics involved. You have to know ahead of time which dataset, metric, and attribute you need to combine to get the answer you're looking for.

Building a basic dashboard

Once you save your new report, you can pop it into a dashboard to see it alongside other key numbers. In the dashboard builder, you can add your saved report as a widget, move it around, and even add filters that control all the reports on the page at once, like a global date range.

Key support metrics (and what’s missing)

Zendesk Explore is pretty good at tracking the standard, historical stuff. These numbers are definitely important for understanding how you've performed, but they often don't give you a clear path forward.

Essential metrics

Here are a few of the most common KPIs you can (and should) build reports for:

Metric	What it Tells You	Common Use Case
Ticket Volume	The total number of new, solved, and open tickets.	Helps with planning team schedules and spotting busy periods.
First Reply Time (FRT)	The average time it takes an agent to send the first reply.	A good gauge of your team's responsiveness and how long customers wait.
Full Resolution Time	The average time from ticket creation to ticket solved.	Shows your overall efficiency and how complex your tickets are.
Customer Satisfaction (CSAT)	The percentage of customers happy with their support.	Measures support quality from the most important perspective: the customer's.

The missing piece: AI-driven insights

The metrics above are great for looking in the rearview mirror, they tell you what happened last week or last month. But what about the questions that help you prevent future tickets and make your team more efficient?

This is where traditional reporting tools often hit a wall. They struggle to answer the more modern, strategic questions, such as:

• Where are our biggest knowledge gaps? What topics are customers asking about over and over that we haven't documented properly?

• What's our real automation potential? Which types of tickets could an AI chatbot handle from start to finish, and how much time would that save us?

• Is our AI even working? If you are using automation, how accurate is it? Where is it getting stuck and handing off to an agent?

Getting answers to these questions requires a different toolset. For example, eesel AI is built to surface these kinds of insights automatically. Its reporting dashboard analyzes incoming tickets to show you exactly where your knowledge gaps are and even has a simulation mode to accurately forecast your automation potential.

eesel AI's dashboard automatically surfaces insights like knowledge gaps.

Challenges and limitations

While Zendesk Explore is a capable tool, it comes with a few trade-offs that are tough for busy support managers to ignore.

• It has a steep learning curve. As we saw in the tutorial, building even basic reports means you need to think like a data analyst. It can take a lot of time to master, time that you probably don't have. In contrast, platforms like eesel AI are designed to be simple and self-serve, letting you get started in minutes.

• The data isn't always fresh. Depending on your Zendesk plan, your data might only refresh once every 24 hours. Even on higher-tier plans, it can be an hour. That makes it tough to spot and react to trends as they happen.

• It only sees what's in Zendesk. Explore is great for reporting on Zendesk data. But what if the answers your agents need live in Confluence, Google Docs, or a Slack thread? Explore can't see that external knowledge, leaving you with blind spots. This is something eesel AI is designed to fix by unifying all your company knowledge, no matter where it lives.

• It's reactive, not proactive. At its core, Explore is a tool for looking backward. You can't use it to safely test out what would happen if you automated a certain type of ticket. This is a critical gap that eesel AI fills with its risk-free simulation mode, which lets you test an AI on thousands of your past tickets to see exactly how it would perform before you turn it on for customers.

eesel AI’s simulation mode allows you to test automation potential risk-free.

A faster path to insights

While Zendesk Explore is for deep-diving into historical data, eesel AI is for getting forward-looking answers you can act on today. It's built to answer the "why" and "what's next," not just "what happened."

With eesel AI, you can:

• Get automated insights. Instantly see what your customers are asking about most, which tickets are eating up your team's time, and where the gaps are in your help center.

• Simulate and forecast with confidence. Know your exact automation potential and how much you could save before you commit to anything.

• See everything in one place. Get a single dashboard that shows you how your AI and knowledge are performing across all your connected tools, not just Zendesk.

Go beyond reporting and start taking action

Getting good at Zendesk Explore is a useful skill for understanding your team's history. But the real goal isn't just to make pretty charts; it's to take quick, informed action. The hours spent trying to build the perfect report are hours that could be spent training agents, filling knowledge gaps, or automating the repetitive work that burns your team out.

If you're ready to shift from reactive reporting to proactive, AI-powered insights, see how eesel AI can connect to your Zendesk account in a few clicks. You might be surprised at how quickly you can get the answers you need to actually transform your support.