conversational-analytics
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The 2% Problem: Why Your QA Scorecard Isn't Telling You About Your Customers
Traditional contact center QA reviews roughly 2% of calls and reports the result as if it describes the whole operation. This page frames the choice between sampling and reading 100% of conversations as an architectural decision rather than a feature difference, comparing how CallMiner, NICE Nexidia, Qualtrics XM Discover, and Spiral by UJET handle coverage, discovery of unknown issues, setup time, and whether output reaches a product team or stops at a QA scorecard. The core argument: a 2% sample tells you about your scorecard, not about your customers.
The 95% You Never Read
Most support teams hand-read roughly 5% of their conversations and make decisions on the other 95% by intuition. This story examines what actually changes operationally when a team reads 100% of conversations across voice, chat, email, surveys, social and reviews using Spiral by UJET: QA becomes real measurement instead of a scorecard exercise, emerging issues surface in days rather than quarters, and the contact-driver list turns into a product backlog. Turo's published three-month results anchor the case: average handle time down 28 seconds, CSAT up 5%, and improved SLA performance.
Matt Clare
Filmed on the expo floor of CCW 2026, this CX Today conversation with UJET's Matt Clare cuts against the deafening tradeshow chorus of 'automate everything.' Instead of using AI to stop customers from reaching out, UJET's newly acquired Spiral analyzes 100% of customer conversations to explain WHY customers are calling, so companies can fix the underlying problem. Clare argues that people still trust people, that AI's real job is to superpower human agents rather than replace them, and that customers deserve choice in how they get help. The talk touches on UJET's brandable mobile and chat SDK, its analytics-to-action loop, and the emerging world of agentic experience orchestration.