Conversation Analytics

Conversation Analytics leverages advanced AI and a Large Language Model (LLM) to analyze customer interactions across all channels, from conversational IVRs to live agent support. It transforms these conversations into actionable data through key analytical functions: transcribing audio, detecting customer emotions, generating interaction summaries, identifying compliance risks, and spotting overarching trends.

These insights are generated in both real-time and post-contact, powering a wide range of applications across the organization:

• Operational Personalization: Dynamically route calls and contacts based on real-time analysis of customer intent, sentiment, and urgency. This enables smarter queuing and increases First Contact Resolution (FCR) rates.

• Supervisor Empowerment: Equip contact center supervisors with live sentiment alerts and conversation summaries, allowing them to provide timely, contextual guidance to agents and proactively intervene in critical situations.

• Streamlined Quality Assurance: Enable QA teams to move from random spot-checks to targeted reviews by efficiently filtering and prioritizing interactions based on specific risk scores, sentiment outliers, or detected topics.

• Strategic Business Intelligence: Provide business leaders with a clear view of the customer voice by tracking emerging topics and trends, informing data-driven decisions on products, services, and customer experience strategies.