How voice retailers can predict customer mood and how they can use that information

In two studies we investigate how voice shopping may provide access to meaningful data on customer mood and how retailers may use such data. In Study 1 we explores the use of a machine learning approach to predict customer mood based on customer commands in the voice shopping process. We compare it to a heuristic approach to customer mood prediction based on situational correlates of mood that that a smart speaker can access (weather, music choice, day of week, and daylight). In Study 2 we explore how a voice retailer could use the potential capability to predict customer mood. Our results provide evidence that a customer’s good mood is associated with purchases of higher-priced premium brands. In addition, retailers can use mood prediction to adapt the presentation of product information to fit customer mood, thus helping customers optimize their decisions. In a sensitivity analysis, we examine what accuracy of mood prediction could enable retailers to use the explored effects.