Call analytics is the collection and analysis of data from voice conversations to measure performance, identify patterns, and drive improvements. Analytics transform raw call data into actionable insights about customer needs, agent effectiveness, and operational efficiency.
What do call analytics measure?
Common metrics include call volume and distribution, average handle time, resolution rates, sentiment trends, and conversion outcomes. For AI voice agents, analytics also track automation rates, escalation triggers, and specific points where conversations succeed or fail.
Why do call analytics matter?
Without analytics, optimization is guesswork. Data reveals which topics generate the most calls, where callers get frustrated, what times see peak volume, and how changes impact outcomes. This visibility lets teams improve the agent based on evidence.
Call analytics in practice
A property management company reviews weekly analytics and discovers that lease renewal questions make up a large share of calls, most of them in the final month of a lease. They configure their AI agent to proactively address renewal options early in calls from tenants approaching lease end, reducing handle time and improving retention.