Voice Analytics is the use of AI to analyze spoken conversations and extract insights such as sentiment, emotion, intent, compliance, talk-time, keywords, and customer behavior. It typically combines ASR (to transcribe calls) with NLP models that score and categorize each conversation at scale. Results feed dashboards, QA scoring, and automated alerts.
Organizations use voice analytics to improve customer service, monitor and coach agents, catch compliance issues, and surface product and churn signals from thousands of calls. Automating this replaces manual call review that could only sample a tiny fraction of conversations. Key criteria include transcription accuracy, real-time vs post-call analysis, sentiment and intent quality, and integrations with CRM and contact-center tools.