Speech Analytics
Speech analytics uses AI and NLP to automatically analyze recorded or real-time voice conversations, extracting insights about customer sentiment, agent performance, compliance, and trending topics.
In Depth
Speech analytics transforms unstructured voice data into actionable business intelligence. Post-call analytics processes recordings to identify patterns: common complaints, compliance violations, successful sales techniques, and agent coaching opportunities. Real-time speech analytics goes further by analyzing conversations as they happen, providing live alerts when a customer's sentiment turns negative, when compliance phrases are missed, or when an upsell opportunity arises.
The technology combines automatic speech recognition (ASR) to transcribe audio, NLP to extract meaning, and machine learning to classify and score interactions. Key applications include automated QA scoring of 100% of calls (vs. manual sampling of 2-5%), churn prediction based on vocal sentiment patterns, competitive intelligence from customer mentions, and compliance monitoring for regulated industries.
GuruSup's analytics capabilities extend speech analytics principles to all channels, providing unified interaction analytics across voice, chat, email, and messaging.
Related Terms
Quality Assurance (QA)
QA in customer support is the systematic process of monitoring, evaluating, and improving the quality of agent interactions to ensure they meet established standards for accuracy, tone, and resolution effectiveness.
Sentiment Analysis
Sentiment analysis is an NLP technique that identifies and classifies the emotional tone behind text, determining whether a message expresses positive, negative, or neutral sentiment.
Voice AI
Voice AI combines speech recognition, natural language understanding, and speech synthesis to enable AI agents to handle phone conversations with customers in real-time.
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