QA and CX answer different questions. QA asks whether the associate followed your process and met your standards. CX asks whether the customer had a good experience. A contact center needs both. Plenty of calls follow every step on the form and still leave the customer frustrated, and some of the best customer moments happen when an associate uses judgment the form didn't anticipate. Analyze scores both so you can see where they agree and where they don't.
We've been automating QA for years, and it's still the foundation that CX insight is built on. The difference is what happens after the score. A scorecard on its own tells you what happened on a call. In Analyze, QA results feed root cause analysis, coaching plans, and training, so the score leads to a decision.
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Every interaction, scored to your standards
Analyze evaluates your calls and chats against your own scorecards, with your criteria, weighting, and rating scales, and not a generic template. Scorecards can be as simple or as detailed as your operation needs, from a handful of compliance checks to a full evaluation form with sections for each call driver.
Self-service calibration
AI scoring is only useful if your team trusts it. Analyze lets your QA team calibrate the AI against their own judgment. They can score a sample of calls side by side with the AI, see where they disagree, and tune criteria definitions until the two line up. Your team does all of this themselves, without a services ticket or waiting on us.
Test before you publish
Scorecards change when policies, products, and priorities change. Before a new or edited scorecard goes live, your team can test it against a set of past interactions and see how scores would shift, so there are no surprises for supervisors or associates the week it launches. Every version is tracked, with the audit trail enterprise compliance teams expect.
Humans in the loop, where they matter
AI scoring covers everything, but some interactions still need a person to review them. Analyze lets QA leads route evaluations to human reviewers by rule, such as a random sample for ongoing calibration, low-confidence AI scores, specific call drivers, or new hires in their first weeks. Assignments can be balanced across your QA team, tracked to completion, and reported on, so a lead can see who is reviewing what and where the backlog is.
A fair dispute process
When an associate disagrees with a score, they can dispute it right in Analyze, with the call and the specific criteria in front of them. Disputes route to the right reviewer, move through a defined approval process, and are logged with the outcome. Disputes also show where a criterion is unclear or where the AI and your team read a standard differently, and that feeds back into calibration.
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