Customer service analytics and performance insights

The numbers in Cuego come from one central list of definitions. It records per measurement which table it comes from, which date column the range runs over and how it is calculated.

So the response time on your dashboard cannot mean something different from the response time in your report. That sounds obvious, but it is exactly where separate reports drift apart: two screens using the same name and doing a different calculation.

Alongside averages Cuego calculates the ninetieth percentile: the time nine out of ten conversations stayed under. That number shows the tail an average smooths away.

An average response time of two hours sounds fine, even when it contains a group that waited three days. That group calls or emails again, and that work comes back into your own inbox. Anyone steering on satisfaction looks at the tail, not the middle.

You set a free date range and can plot two measurements against each other, including from different modules. For example the number of conversations alongside the average handling time.

A number on its own says little. More tickets is good news for a growing webshop and bad news at flat revenue, and you only see that difference when both lines sit in the same picture.

You see which topics keep coming back and how many of them Cuego handled itself. That is the list you start automating from, because that is where the volume sits.

You start automating with the question that gets asked most often and can go wrong least often. Not with the hardest question, even though that is the one your team suffers from most.