Speech analytics converts audio from customer calls into searchable text and then applies algorithms to detect keywords, phrases, sentiment, silence, and talk-over rates. It can operate post-call (batch analysis) or in real time to prompt agents with suggested responses during a live conversation.
Why it matters: Support leaders can audit far more interactions than manual QA (quality assurance) allows—often 100% of calls—making compliance monitoring, root-cause analysis, and agent coaching more data-driven and scalable.
Common use cases:
- Identifying the top reasons customers call (contact-driver analysis)
- Flagging regulatory compliance gaps (e.g., required disclosures not stated)
- Detecting customer frustration signals like raised voice or repeated phrases
- Scoring agent adherence to scripts or soft-skill behaviors
A typical output is a dashboard showing which product issue drove a spike in call volume, allowing teams to push a self-service fix before the next wave of contacts arrives.