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How Voice Analytics Can Improve Customer Service

detective According to Technavio, a research firm, the global voice and speech analytics market is poised to grow by $2.17 billion during 2019-2023, progressing at a compound annual growth rate of almost 18% during the forecast period. More businesses are recognizing that this technology can transform their customer experience (CX) while helping to drive greater efficiencies across their call centers.

One of the key highlights from the Technavio research is that voice analytics and speech analytics have the ability to give organizations the ability to adapt to new trends and changes in consumer behavior. Consumers have had to rapidly modify their behavior this year, and many will have become more comfortable with technology as a result. That familiarization will give rise to a greater level of expectations, and voice analytics may help meet those demands.

Voice analytics give the user the ability to better understand a customer’s perspective through their voice and tone. The pitch, loudness, timbre, speech rate, and pauses in a customer's voice will help to analyze and assess whether a conversation is proving to be a positive or negative experience for them. This will be a two-way conversation, so you can also measure your agent's tone of voice, too. This insight will help you to understand how they are feeling during a conversation, while at the same time measuring sentiment based on the words used.

Voice analytics has the potential to take the heat out of a tricky situation, and can also provide data that you and your team can use to reduce call times and repeat calls. This, in turn, will help to reduce customer churn and improve the quality of the customer experience. As Gartner recently highlighted, 94% of customers with low-effort interactions intend to repurchase, compared with 4% of those experiencing high-effort interactions.

Some may want to take voice analytics a step further, and learn how they can utilize a combination of language analytics and behavioral analytics (such as topic modelling, natural language processing (NLP), and vocal emotion detection) to create greater insight into agent and customer conversations. From this crucial data, you can extract actionable insights and introduce quick changes to improve agents’ performance, while analyzing calls to help them to identify complaints.
Ensuring that you’re able to react to vulnerable customers means that you can rapidly transform your customer experience. With embedded machine learning, you can continue to learn and adapt to your customers’ ever-changing needs.

 A version of this story first appeared in Finance Derivative