Contact center analytics has reached an awkward stage. Many vendors can now say they analyze 100% of customer conversations. Yet, according to Taylor Wittman, Senior Solutions Consultant at UJET, that capability does not guarantee better coaching, improved quality assurance, or customer outcomes.
In a CX Today interview with Rob Wilkinson, Wittman argued that the industry has moved from one problem to another. Contact centers once struggled with too little interaction data. Now, many teams have more conversation data than supervisors can realistically use.
The gap matters because quality assurance (QA) and coaching still shape how agents improve, how supervisors prioritize support, and how contact centers connect customer conversations to operational change.
UJET Says 100% Analysis Is Now Table Stakes
Wittman works closely with contact center buyers and said a familiar question now appears in almost every evaluation: can the platform analyze all calls?
That question used to carry more weight. Historically, QA teams may have reviewed around two percent of interactions, often selecting a small number of calls from each agent.
Full conversation analysis changes the available sample. However, Wittman warned that it does not automatically change the operating model. Wittman framed the issue through the buyer conversations she has every week:
“Every vendor can analyze 100% of these interactions. But that’s table stakes now.”
That issue has become more pressing as AI budgets face scrutiny and leaders ask what contact centers are actually learning from the data they collect.
Supervisors Need Guidance, Not Another Dashboard
Whitman’s strongest operational point is that many analytics tools were designed for analysts, not supervisors. Analysts may have time to explore trends, query data, and build reports. Frontline supervisors often need to make quick decisions about escalations, coaching priorities, and agent performance.
A dashboard may impress a QA team. Yet it may not help a supervisor decide what to do on Monday morning. Wittman described the disconnect:
“When you ask the supervisor what difference this makes in their day-to-day, they don’t really know.”
That is the supervisor gap inside 100% analytics: the data may be available, but the action path remains unclear.
Supervisors need to see patterns tied to specific agents, specific skills, and specific customer outcomes. They also need recommendations that fit their time constraints. Without that translation layer, conversation analytics risks becoming another system to check.
Coaching From Every Conversation Changes the Tone
Traditional QA can feel punitive because small samples carry too much weight.
Wittman gave a simple example. An agent might handle 1,000 calls in a month, while QA reviews only three or four. If one of those calls happened on a difficult day, that small sample can shape the agent’s score, feedback, and confidence.
That can make agents defensive, even when supervisors are trying to help. Full conversation analysis changes the coaching conversation because it gives supervisors a broader view of performance over time. Wittman explained how that changes the agent experience:
“It’ll be less about surveillance or docking points, and more about uplifting agents and progressing their development.”
A supervisor can point to patterns rather than isolated failures. An agent can see where they are improving, where they are strong, and where coaching is genuinely needed. Coaching becomes less about catching mistakes and more about development.
Analytics Must Be Traceable to Real Calls
UJET’s argument is not simply that more interactions should be analyzed. Wittman emphasized the need for “traceable answers.”
In practice, that means supervisors and operations leaders should be able to move from a pattern to the real calls behind it.