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. 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 industry, customer support manager Kailey Maldonado spent considerable time sitting with that problem.
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 only by watching her correct its mistakes over time.
The result was data that was organized enough to be directional, but not reliable and specific enough to be actionable.
When Kailey needed to make a case upward or cross-functionally, that distinction mattered.
“It’s one thing to say, hey, I have a bunch of users reaching out about this topic. It’s another thing to come out with specific issues and numbers that quantify how serious an issue is.”
Without that specificity, support’s read on the business remained just that: a high-level read, largely unusable by the teams whose roadmaps it should have been shaping.
RETR adopted Klarion to deeply analyze customer tickets and surface patterns across them. Rather than training the system through correction loops, Kailey could describe in plain language RETR’s business, its products, and how issues should be classified, and the platform reasoned from there. Klarion extracts multiple issues from a single ticket so nothing gets missed, and assesses the level of customer frustration tied to each one, adding business impact context that raw ticket counts never could. Most importantly, it identifies specific repeating issues that are trending and prioritizes them by support load and customer pain.
That combination of completeness, trend detection, and impact assessment changed what she could do with support data.
Billing became the test case. RETR’s customers had limited self-service access to their billing information, and Kailey had flagged it as a recurring problem area. But “billing is a problem” is not a product brief. With Klarion, she could show exactly what customers were trying to do, how often, and with how much frustration: confirm payment details, pull invoices, update a card on file. Distinct problems, each requiring a different solution.
She brought those breakdowns to engineering on a weekly basis, along with Klarion’s analysis of each repeating issue. The billing self-service module RETR is now building was scoped directly from that data.
“Our engineering team was able to get a clear picture of what needed to be fixed, just from the data I was presenting each week,” she said.
The risk of invisible patterns becomes even more pronounced as companies route more volume through AI agents. A closed ticket looks like a resolved problem. It isn’t always.
RETR was using an AI agent to respond to questions about account lockouts triggered by its rate-limiting security logic. The agent explained the situation clearly, users understood it, and tickets closed without escalation. By the numbers, it looked like a solved problem.
Kailey saw it differently. The AI was handling the explanation. It wasn’t addressing the friction. She traced the pattern upstream: could the initial lockout notification email be clearer, so users never needed to contact support at all? Could two-factor authentication reduce false positives in the security logic?
The point isn’t that AI deflection is the wrong tool. It’s that deflection without visibility into the underlying pattern can paper over product and process problems that compound over time. The ticket closes. The root cause doesn’t.
The cross-functional use has expanded. Kailey now brings customer-level support-health data to RETR’s sales team, flagging which enterprise accounts have encountered frustrating problems. That context has changed how sales leaders frame certain demos and training.
Her broader approach: support data is relevant to product, marketing, sales, and engineering, but only when it’s translated into terms each team uses. Raw ticket counts don’t create urgency. Specific customer impact, tied to accounts and revenue, does.
“If support is just seeing support metrics and support numbers, that’s not going to be relevant to what marketing needs to hear or what product needs to hear,” she said. “They need to hear it in a perspective that fits with their role.”
Asked what she would miss most if Klarion were removed, Kailey didn’t cite a feature. She cited time.
“I get a lot of my time back being able to use Klarion to deep dive into those issues for me,” she said.
Time previously spent tagging, correcting, and manually constructing a case for a trend now goes toward acting on what the data surfaces. That shift, from debating what’s happening to doing something about it, is what changes the conversation with the rest of the business.
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