Using NLP to analyze whether customer statements comply with their inner belief
This addresses the challenge of identifying complex emotions like truthfulness in customer interactions, particularly for service industries such as insurance and job interviews, though it appears incremental as it builds on existing emotion detection methods.
The research tackled the problem of detecting whether a customer is speaking truthfully or against their inner belief, using AI and speech analysis, achieving an accuracy of around 98% in an experimental setting with 40 participants.
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.