Questions fréquentes sur le Speech Analytics

The reliability of Speech Analytics depends on three factors: the quality of the recordings, the performance of the transcription and analysis engines, and the configuration settings. Strong accents, fast speech, and jargon remain areas of concern, and human oversight remains essential.

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.

Speech Analytics transforms calls into actionable data: transcription, detection of topics and key phrases, sentiment analysis, and presentation in dashboards and alerts. When extended to emails and chats, it becomes conversation analytics.

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. …

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.