Advanced analytics, rooted in artificial intelligence (AI), is a game-changer for customer insights. But using text-based analytics models on voice communications will not be a fruitful exercise. Instead, voice interactions require use of a speech analytics platform, with models that have been built and finely tuned specifically for the unstructured nature of voice communications.
Such analytics tools are able to provide the contextual accuracy needed to render the best results, by taking into account not just the words, but sentence structure, and a wide range of acoustics that are at play in every spoken interaction. Even better is to adopt a multi-channel analytics strategy, as capturing feedback from text-based channels and correlating those with speech-based channels adds perspective and enables you to get a complete picture of the customer journey.
However, as businesses analyze customer interactions, many fail to distinguish between the models required for text-based channels and voice calls. That means they transcribe their voice calls into text and run it through models built for text only. Falling for the myth that text analytics and speech analytics are interchangeable can be all too tempting in an age in which pre-trained models for text are readily available for enterprise use out of the box.
Read this paper on why speech and text models are different produced by No Jitter, the industry’s leading source of objective analysis for enterprise communications.