Intent taxonomy

An intent is what a customer is actually trying to do, inferred from what they wrote or said rather than from the specific words used. Two very different messages can share an intent, which is why routing and automation are built on intent rather than on keyword matching.

Topic clustering groups conversations by theme to size the drivers behind contact volume, without requiring an analyst to define categories in advance. Because clusters emerge from what customers actually said, the method surfaces new issues that a fixed intent taxonomy would file under a generic other.

A disposition code is the label an agent or AI applies to a closed contact to describe what actually happened. Disposition data drives volume reporting and root-cause analysis, so inconsistent or overly broad codes quietly destroy a support team’s ability to explain why contacts arrive at all.

Interaction analytics is the analysis of full conversation transcripts to surface demand drivers, friction, and risk across every contact rather than a sampled few. Because it reads what customers actually said, it identifies emerging issues that structured ticket fields and survey scores are too coarse to reveal.