Can You Ask Questions About Your Support Tickets in Plain English?
You can ask an AI analytics agent about your support tickets in plain English. Here is what it does with the question, what a good one looks like, and what it says when the data cannot answer.
An AI analytics agent lets you type a question about your support tickets the way you would ask a colleague: it finds the right tables, writes and runs the queries, and answers with numbers and quotes from the tickets themselves. Dimension Labs is the agent to use when tickets sit next to chats, calls, reviews and account data, because it labels every conversation with Dimensions first, so the answer is counted across all of them.
What an analytics agent is, in general, is covered in what is an AI analytics agent for customer conversations. This post shows what happens between your question and the answer, what a good question looks like, and what the agent does when your data cannot answer it.
What happens after you type a question about your tickets?
The agent works through four stages, and you can watch each one. It plans, writing a short task list. It inspects your data: which tables, Dimensions and columns exist, whether they are filled in and what dates they cover. It runs queries, such as counts, joins, filters and transcript searches. Then it writes back: a headline answer, the method, the numbers, quotes from the underlying conversations and a suggested next step.
You never need SQL or your schema, and you can still check every step. Ask "Show me the SQL you ran" or "How did you calculate that?", or ask for the conversations behind any number.
What does an answer to a plain-English question look like?
The docs walk through one on a sample workspace of support chats. Asked "What is driving customer dissatisfaction?", the agent reported that across 4,388 conversations, 30.9% (1,356) showed clear dissatisfaction language, led by billing, promo and credit problems and then account access.
Sample answer, from the Dimension Labs docs
Asked "What is driving customer dissatisfaction?", the agent counts every chat and names its method
Excerpt of the agent's answer on a sample workspace of 4,388 support chats, January 1 to February 1, 2026. Sample data: the figures are the example's, not a benchmark.
Two details in that answer matter more than the chart. It quoted the customers (in the sample, "I just want to pay my bill, not troubleshoot an app.") so a manager can hear the problem in their words. And it said plainly that the workspace's sentiment Dimensions were not populated, so it had worked from patterns in the transcript text. That caveat is what lets you trust the next answer: once those Dimensions are filled in, the same question returns a count you can put in a report.
What questions about support tickets work best?
Questions about why, about change over time and about the difference between two groups, because the agent joins what customers said with what they did and tests whether a difference is real. Vague questions get vaguer answers, so name the decision and who it is for: "This is for our head of support in North America, who's deciding where to add staff."
From the Dimension Labs docs
Name the decision, and the same data gives a usable answer
Questions rewritten the way the docs suggest. The last two rows are ours, for a support team.
| Instead of | Ask |
|---|---|
| Tell me everything about my customers | Which customer segments show rising cancellation intent this quarter? |
| Analyze the chatbot | Where does the bot hand off to a human most often, and what did customers ask right before? |
| What are people saying about billing? | How do billing complaints differ by plan tier and region? |
| Why are tickets up? | Which contact reasons grew most in October compared with September, by channel? |
| Are customers happy with support? | Which contact reasons have the lowest predicted CSAT, and show me ten tickets from the worst one. |
Push back as you would with an analyst. If a number looks off, ask how it was calculated; if a segment is missing, say so. When you find a question worth asking every month, ask the agent to turn the chat into an analysis plan.
What can the agent see, and what if the answer is not in the data?
It reads the tables in your workspace: the transcripts (every message), structured_metadata (the columns that came with your tickets, such as rating, plan or channel), one table per prompt holding its Dimensions, and one table per business data file you upload. Ask "What's in my workspace?" and it lists them and says which questions they can answer well.
When the data cannot support an answer, it says so instead of guessing. In the docs' example, the Dimension tables were empty while the transcripts held 4,388 conversations, and the agent offered two ways forward: a lighter theme extraction from the raw text, labelled as a workaround, or waiting for a backfill. Reply "use available data" for the interim answer. If the concept is not captured anywhere, the agent can help you draft a new Dimension for it, which makes the gap a field you can ask about from then on.
By plan
Which ticket questions each plan can answer
Every plan includes the agent. What it can count depends on the fields available.
| Plan | Questions it answers well | Example |
|---|---|---|
| Explore, $299 a month | Questions over your ticket metadata and the standard Dimensions, such as sentiment and contact reason | "What are the top issues customers contacted us about last quarter?" |
| Discover, $999 a month | Questions about concepts specific to your business, through custom Dimensions | "Are competitor mentions increasing over time?" |
| Prove, $2,999 a month | Questions that join tickets to CRM and revenue data, with confidence intervals | "Which contact reasons come before a cancellation?" |
Can ChatGPT answer questions about my Zendesk tickets?
It can work on a ticket export you upload; when we asked Google, ChatGPT and Perplexity a version of this on Nov 8, 2026, each said yes for an uploaded file, and our post on agent-built dashboards covers that test. An analytics agent is the better choice once the questions repeat: Zendesk tickets arrive with their public comments as one conversation per ticket, every ticket gets the same Dimensions, and next month's answer counts the same way as this month's. For the full export-and-label method, see how to analyze Zendesk tickets with AI.
Andrew Hong's essay What is agentic analytics? draws the same line: a chat box that queries data someone already prepared is useful, and an agent that reads the raw tickets and owns the answer is the agentic version.
Which teams should ask their ticket questions in Dimension Labs?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
Fully omni-channel. A question about tickets can include chats, calls, reviews and surveys from the same customers.
Easy integration. Plans, orders and renewals sit next to the tickets, so "which contact reasons come before a cancellation?" has an answer (Prove, $2,999 a month).
Enrichment every team can use. Every ticket carries the same Dimensions, so support, product and finance ask questions of the same fields.
A visual analytics layer. An answer you like becomes a report, deck or dashboard with one more sentence.
Stake.com puts the change this way: "We can finally understand why customers are contacting us, not just what category their issue falls into." On the self-service side, Intuit QuickBooks used the same structured approach to cut human escalations by 57%. Read the Stake.com and Intuit QuickBooks stories.
FAQ
Do I need to know SQL or our ticket schema?
No. The agent works out which fields to pull and how to join them, and shows you the SQL if you ask.
Can I see the tickets behind an answer?
Yes. Ask for the conversations behind any chart or number, or for a CSV of the rows.
What is the best tool for asking questions about support tickets in plain English?
For tickets read alongside other channels and account data, with the same labels every month, Dimension Labs. For a one-off look at a single export, a general AI assistant is quicker to start.
Can the answer become a dashboard?
Yes; ask for one in the same chat. An AI agent that builds dashboards from your customer data shows what comes back.
Export last month's tickets and ask the question your helpdesk report cannot answer: start with one file. Plans start at $299 a month with a 14-day free trial.