A member of our Sales team will follow up within one business day. In the meantime, feel free to explore how Klarion turns support conversations into a clear, executive-ready view of what’s driving customer pain.
Stay updated with Klarion on LinkedIn for product news and customer experience insights.
Explore articles on support insights, product signals, and customer experience.
McKinsey recently found something counterintuitive about AI-driven customer care: a bank’s AI agent can hit a 90 percent containment rate on disputes, resolving nine out of ten calls without ever escalating, and the bank’s total cost per contact can still go up. If the customer calls back three days later because the root cause was never fixed, the bot didn’t solve anything. It just added a step. McKinsey is careful to note this isn’t a failure of the AI itself. It’s a failure of treating a technical metric, containment, as if it were a business outcome. That gap between “ticket closed” and “problem solved” is playing out across every industry putting AI in front of customer support. Containment rates climb, and executives assume satisfaction is holding steady. But from the customer’s perspective, closing a ticket isn’t the same as solving the problem. They still hit a wall, still had to seek help, and still had to go through a resolution process. When teams measure success solely by containment rate, they miss a critical reality: managing case volume often hides the root causes of customer pain. The Illusion of a “Solved” Problem Kailey Maldonado, Support Leader at RETR, ran into this...
In most SaaS companies, the support team sees things that the rest of the organization doesn’t. They hear from customers daily and watch the same frustrations cycle through the queue week after week. But when ticket volume is high, and resolution is split across multiple team members and AI agents, no single person holds a complete picture. Individual tickets get handled. The patterns underneath them stay invisible. That gap between handling customer issues and quantifying trends is where product quality and customer experience quietly deteriorate. Retention risk builds. Support load compounds. And because no one has the full picture, the organization keeps treating symptoms rather than causes. At RETR, a software platform serving the mortgage and real estate industry, customer support manager Kailey Maldonado spent considerable time sitting with that problem. Good Instincts, Hard to Prove RETR’s support queue runs around 500 tickets a week, handled through HubSpot Service Hub. Kailey had built a disciplined categorization workflow: every closed ticket required a topic tag, creating a record she could theoretically report on. In practice, the system had real limits. HubSpot’s AI categorization applied one topic tag per ticket, couldn’t be given context about what a topic actually meant, and improved...
We’re proud to announce that Klarion is now an officially approved Zendesk technology partner for AI, with our app listed in the Zendesk Marketplace. That is an important milestone for us, but more importantly, it gives Zendesk customers a new way to understand what their support conversations are really saying. Zendesk is where customer pain often shows up first. A customer gets stuck. A workflow breaks. A billing issue repeats. A product gap becomes visible. A frustrated account signals that something is wrong before it appears in a renewal conversation, an NPS survey, or a churn report. Support teams see this pain every day. The problem is that the signals are buried inside thousands of tickets and the patterns are writ across them. That is where Klarion comes in. Zendesk Runs Your Support. You Still Need to Understand Customer Pain. Zendesk is exceptional at what it was built to do. It routes tickets, tracks SLAs, measures agent performance, and keeps support operations running smoothly. For the teams who depend on it, it’s foundational. But once the ticket is closed, critical questions often go unanswered: Why did that customer need to reach out in the first place? How often does that...
