Questions fréquentes sur le Quality Monitoring automatisé

The benefits of automated " Quality Monitoring " via Speech Analytics are numerous and well-documented. They can be grouped into several major categories:

Manual Quality Monitoring relies on evaluators who listen to or read a sample of interactions. Automated Quality Monitoring applies the same evaluation framework using AI to a much broader scope, which you define based on your business needs and budget. In CrossCX, both methods use the same evaluation framework and reports, and can be combined.

The introduction of the “ Speech Analytics ” may raise questions—or even concerns—among existing customer service representatives and quality analysts. It is therefore important to anticipate the impact on employees and to manage the change in a transparent and positive manner.

In theory, one might think that AI is enough and that we could “listen less to our customers.” “That’s not a good approach,” warns one expert— Speech Analytics , on the contrary, should be viewed as a way to improve your ability to listen. …

A successful project starts with a specific objective, verifies the quality of and access to recordings, brings together business units, the IT department, and the legal department, begins with a pilot scope, and then expands. The approach is just as important as the technology.

The results are broken down into four categories of metrics: evaluator productivity, advisor performance, customer satisfaction, and compliance. These are measured by comparing pre- and post-results for the quality metrics already being tracked, and then in the quality reports.

The field of conversational analytics and automa Quality Monitoring s is evolving rapidly, driven by advances in AI and the changing needs of businesses.