  Customers' emotions play a vital role in the service industry. The better
frontline personnel understand the customer, the better the service they can
provide. As human emotions generate certain (unintentional) bodily reactions,
such as increase in heart rate, sweating, dilation, blushing and paling, which
are measurable, artificial intelligence (AI) technologies can interpret these
signals. Great progress has been made in recent years to automatically detect
basic emotions like joy, anger etc. Complex emotions, consisting of multiple
interdependent basic emotions, are more difficult to identify. One complex
emotion which is of great interest to the service industry is difficult to
detect: whether a customer is telling the truth or just a story. This research
presents an AI-method for capturing and sensing emotional data. With an
accuracy of around 98 %, the best trained model was able to detect whether a
participant of a debating challenge was arguing for or against her/his
conviction, using speech analysis. The data set was collected in an
experimental setting with 40 participants. The findings are applicable to a
wide range of service processes and specifically useful for all customer
interactions that take place via telephone. The algorithm presented can be
applied in any situation where it is helpful for the agent to know whether a
customer is speaking to her/his conviction. This could, for example, lead to a
reduction in doubtful insurance claims, or untruthful statements in job
interviews. This would not only reduce operational losses for service
companies, but also encourage customers to be more truthful.
