Let's make every customer experience better than the last, together.
Every company has a theory about why its numbers look the way they do. Handle time is up because of the new policy. CSAT dropped because the new hires aren't ready. Sometimes the theory is right, but more often it's a guess nobody has tested. Analyze treats every root cause as a hypothesis and tests it with data science against several independent sources of evidence. Leaders get an answer they can act on, and one they can defend when someone asks why.
What top performers do differently
The first source is discovery. Analyze runs an open-ended, unstructured analysis of your top performers' conversations and compares them with everyone else's. It doesn't start from your scorecard. That surfaces behaviors your QA form never thought to look for, like how a top associate frames a fee, or the question they ask before troubleshooting starts.
Which behaviors move outcomes
The second source is correlation. Analyze measures how specific skills and behaviors relate to the outcomes you care about, including KPIs like handle time, conversion, and first contact resolution, and the four elements of CX. That separates the behaviors that feel important from the ones that actually move results.
The same answer, every time
A root cause is only useful if it holds up. When discovery and correlation point to the same behavior, confidence goes up. When they disagree, Analyze flags it instead of choosing one. The method is consistent, so the same data produces the same conclusion whether you run it this week or next quarter. The result is a finding you can build a coaching plan, a training program, or an AI agent design around without relitigating it every month.
Product Team