Measure your support operation with conversation analytics

You can't improve what you can't measure. The support operation is no exception. Every conversation is recorded with data such as whether it was resolved, whether it was handed off to a human, how long it took and whether the customer left satisfied.

You monitor these metrics in real time from a single panel. A drop in resolution rate, a sudden rise in handoffs or response times stretching on a particular topic are early signals of a problem. You see the issue before it turns into a customer complaint.

Which signals do we extract?

Agent Skript extracts a few layers of insight from every conversation. Sentiment analysis tells you how the customer felt throughout the call; topic classification tells you which category the request falls into; the automatic summary gives an at-a-glance view of the conversation. A CSAT score shows how satisfied the customer was when they left.

These layers gain meaning together, not in isolation. If, for example, CSAT is low on a particular topic while the sentiment curve also turns negative, that's a clear experience problem that needs fixing right there.

Unanswered questions: the most valuable report

Analytics isn't only about looking back. By seeing which questions are asked frequently, you close gaps in your knowledge base and identify and improve the scenarios where the assistant is weak. This way the automated resolution rate increases over time.

The 'unanswered questions' report in particular is a gold mine for your product roadmap. Requests customers ask but the assistant couldn't handle point both to a gap in the knowledge base and sometimes to a real gap in the product or process itself.

From data to action

Team planning also becomes clearer with this data. When you know at which hours and on which topics demand rises, you position your human resources where they're truly needed. Seeing that after-hours demand is high alone makes the case for keeping the assistant open 24/7.

The numbers in the panel are real data from your own operation; compare them not with industry averages but with your own last week. Break improvement into small, measurable steps: close a gap, watch its impact for a week, then move on to the next.

In short, conversation analytics takes your support operation out of being a 'black box'; it turns it into a measurable system that gets a little better every week.