Speech analytics

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.

Sentiment analysis scores the emotional tone of a conversation to flag risk and measure experience without waiting for a survey response. Applied across every contact rather than a sampled few, it covers the large majority of customers who never complete a satisfaction survey at all.

Quality assurance is the structured review of conversations against a scorecard, applied to human agents and AI agents alike. Traditional QA samples a handful of conversations per agent each month; automated QA scores every conversation, which changes the exercise from spot-checking into actual measurement.

Text analytics automates the process of extracting structure and meaning from unstructured written feedback: support tickets, chat transcripts, survey verbatims, reviews, and social posts. Core techniques include classifying text into predefined topic taxonomies, extracting named entities such as products, features, and locations, modeling topics to surface unlabeled themes, and tagging a sentiment score to gauge tone. The practical output is a count of how often each issue appears and how that frequency trends over time. Results are only as useful as the underlying taxonomy: a vague or inconsistent category structure produces noisy, hard-to-action data. OnClarity’s Voice of Customer applies these techniques across feedback aggregated from 100+ sources.