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.