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.
What is Real-Time Speech Analytics?
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.
Benefits of Real-Time Speech Analytics
- Improve performance – It highlights words, phrases, and other verbal cues (e.g., prolonged hesitation) that could indicate an agent is faltering. The supervisor receives an alert and can immediately barge into the call in real-time
- Maintain compliance – If an agent skips an important keyword or phrase that is mandated by compliance norms (like consent), real-time speech analytics flags it immediately. The agent can correct themselves to avoid a compliance violation
- Cross-sell/upsell – Real-time speech analytics can detect verbal indicators of interest. This tells an agent when would be the perfect time to recommend an upgrade, suggest an additional product/service, and unlock more revenue opportunities
- Enhance CX – If the customer displays signs of frustration or disinterest, the analytics solution would recommend that the call is transferred or that someone else intervenes. This improves the quality of experience and completes calls within the standardised duration
- Offer coaching – The sentiment analysis component of real-time speech analytics can highlight when an agent is facing a difficult or problematic situation. Supervisors can convert these into learning moments, observing the agent and offering tips without any intervention
Caveats to Remember When Implementing Real-Time Speech Analytics
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.
Tips for Maximising Real-time Speech Analytics
- Augment the technology with agent training and detailed scripts
- Combine real-time analytics with post-call analysis for holistic insights
- Ensure the UX/UI design is non-intrusive
- Constantly improve the algorithm by feeding it call data
