How voice retailers can predict customer mood and how they can use that information
Ingo Halbauer and
International Journal of Research in Marketing, 2022, vol. 39, issue 1, 77-95
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.
Keywords: Voice shopping; Mood; Mood prediction; Choice architecture (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijrema:v:39:y:2022:i:1:p:77-95
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