Enhance CX and improve performance
Real-time speech analytics dramatically improves upon the capabilities of traditional, historical analysis. It can automatically flag important events in an ongoing call based on pre-configured business rules, and even learning from how agents act on alerts. Real-time speech analytics solutions and services are expected to give a massive leg-up to the overall speech analytics market, suggests research.
Here is a quick guide to everything you need to know about maximising this technology at your contact centre.
Real-time speech analytics can be defined as an analytics and contact centre intelligence solution that uses natural language processing (NLP), sentiment analysis, and other AI techniques to highlight keywords in an ongoing call, mention why they are important and recommend an action to the agent taking the call.
It is primarily meant for short-term outcomes, helping agents course-correct before a conversation turns problematic or an opportunity is missed.
Like most forms of AI, real-time speech analytics isn’t failproof. To begin with, there might be a lag of a few seconds or even microseconds between an utterance and its analysis outcomes. Without training, the agent would simply sit waiting, exasperating the customer.
Further, real-time speech analytics are only as good as the business rules powering them — make sure that you invest in historical speech analysis, trend recognition, call scripting strategy, and customer behaviour analysis before relying on real-time recommendations.
The UX design of real-time speech analytics technology could also prove distracting in some cases, holding the agent back from focusing on a live conversation and real verbal indicators.