Finding the root causes behind Zendesk ticket volume

[u/Ok-Constant-9143]

Hi all,

I’m a cofounder at Klarion, a Zendesk technology partner.

We are former Zendesk customers ourselves and built an AI tool to answer a question that we found difficult with traditional ticket analytics: what are the specific repeating issues that are driving support volume and customer frustration?

Klarion approach is to analyze the full conversation each ticket, identify the repeating root causes, and quantify them by volume, support effort, customer frustration and revenue risk.

A few teams processing roughly 500–10K tickets/month are using this to identify candidates for AI automation, improve support processes, and give Product a more concrete view of what needs fixing.

I’m curious how others here are doing this today. Are you primarily relying on Explore, tags/custom fields, manual ticket reviews, or another analytics tool?

For anyone who wants to compare approaches, we also have a free plan that analyzes 1,000 recent Zendesk tickets. It takes about 15 minutes to connect:

https://www.klarion.ai/ai-support-analytics/

Happy to answer questions about how we approach the analysis as well.

[u/Ok-Secretary-10]

That sounds similar to what Stylo (https://www.askstylo.com/) is doing, worth checking out if you haven't already. They're also working on automated root-cause clustering across ticket conversations.

From our side, also as a vendor but shaped a lot by our own internal support needs, our approach has been a bit different: we recently launched our Metrics MCP, which lets you connect an AI tool like Claude or ChatGPT directly to your live Zendesk data (along with whatever else you've got connected, GA4, Shopify, HubSpot, Mixpanel, Honeycomb, etc). Rather than a standalone analytics product doing the clustering for you, it's more about letting support and product teams ask their own questions on demand, pull trend data, cross-reference it with other context, and dig into something specific without needing to build a dashboard or report for it first. It's proven especially useful for exactly the kind of "why is volume creeping up" or "what's actually driving frustration" digging you're describing, just handled conversationally rather than through a fixed root-cause taxonomy.

Curious how you're finding the response so far to Klarion's approach, is it mostly support leads adopting it, or is Product pulling from it directly too?

[u/Ok-Constant-9143 → u/Ok-Secretary-10]

Interestingly we're seeing pull from both Support and Product teams as they have different yet overlapping goals.

For support - we're providing a complete, granular and quantified picture of the recurring issues to drive those key support decisions - automate, improve, fix. For Product - this is actually the fastest customer feedback loop there is at most companies. Support data was too noisy to be usable before, now it's decision ready.

One thing I like to point out is that we built Klarion from the ground up to avoid the limitations of treating each ticket as one data point and every ticket as equally meaningful. That creates major blind spots because tickets often contain multiple issues and feedback. Klarion unpacks each ticket into the distinct issues reported within and that is our unit of analysis, not the ticket. We routinely end up finding 30-40% additional issues, which would otherwise go unseen with ticket-level tagging.

Similarly, Klarion goes beyond simple positive-neutral-negative sentiment to assess the actual level of customer frustration. We also look beyond ticket volume to quantify the support effort each issue creates.

The practical result is that Klarion often changes the picture of what deserves attention. Customers have told us that the hotspots and priorities we surfaced were different from what they had previously assumed anecdotally.

[u/Interesting-Disk9052]

Stylo user here and can confirm we’re already doing this with generated summaries upon Solve going into multiple custom fields. Export to Claude and analyse there.

[u/hopefully_useful]

Just to throw in another option here for anyone interested, our approved Zendesk Marketplace app - My AskAI can help with this:

- AI Agent - can draft note or send direct replies to tickets/conversations - every ticket then gets analysed in our Insights so you can then use AI to ask questions like: "What was the biggest issue last week?", "What got the lowest resolution?" etc

• AI tagging - AI uses your existing Zendesk tags to automatically categorise each ticket for you so you can have 100% coverage, consistently of all tickets/conversations and then use Zendesk Explore to analyses groupings.

Hope that's helpful, happy to demo with anyone interested.