Conversational analytics is the analysis of text and voice conversations to extract insights about customer needs, agent performance, and operational opportunities.

Conversational analytics transforms chat logs and call recordings from archives into intelligence. What are customers actually asking about? Where do conversations get stuck? Which issues correlate with negative sentiment? What language predicts escalation? Analytics answers these questions at scale, processing thousands of conversations to surface patterns.

Core capabilities include: topic clustering (grouping conversations by subject), sentiment tracking (monitoring customer emotion across interactions), intent analysis (understanding what customers want), agent evaluation (comparing handling approaches), and trend detection (identifying emerging issues). Advanced analytics adds predictive modeling—forecasting volume, predicting escalation risk, identifying churn signals.

For CX leaders, conversational analytics is the foundation of continuous improvement. Without it, you're relying on anecdotes and sampled QA reviews. With it, you can identify that 23% of conversations about topic X result in escalation, or that customers who use phrase Y are significantly more likely to churn. This drives targeted intervention rather than generic improvement efforts.

Related terms: AI observability, Sentiment analysis, Intent recognition