by Avi Gesser, Johanna Skrzypczyk, Robert Maddox, Anna Gressel, Martha Hirst, and Kyle Kysela
From left to right: Avi Gesser, Johanna Skrzypczyk, Robert Maddox, Anna Gressel, Martha Hirst, and Kyle Kysela
There is a growing trend among customer-facing businesses towards using artificial intelligence (“AI”) to analyze voice data on customer calls. Companies are using these tools for various purposes including identity verification, targeted marketing, fraud detection, cost savings, and improved customer service. For example, AI voice analytics can detect whether a customer is very upset, and therefore should be promptly connected with an experienced customer service representative, or whether the person on the phone is not really the person they purport to be. These tools can also be used to assist customer service representatives in deescalating calls with upset customers by making real-time suggestions of phrases to use that only the customer service representative can hear, as well as evaluate the employee’s performance in dealing with a difficult customer (e.g., did the employee raise her voice, did she manage to get the customer to stop raising his voice, etc.).
Some of the more novel and controversial uses for AI voice analytics in customer service include (1) detecting whether a customer is being dishonest, (2) inferring a customer’s race, gender, or ethnicity, and (3) assessing when certain kinds of customers with particular concerns purchase certain goods or services, and developing a corresponding targeted marketing strategy.