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

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  • Halbauer, Ingo
  • Klarmann, Martin

Abstract

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.

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  • Halbauer, Ingo & Klarmann, Martin, 2022. "How voice retailers can predict customer mood and how they can use that information," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 77-95.
    • Handle: RePEc:eee:ijrema:v:39:y:2022:i:1:p:77-95
    DOI: 10.1016/j.ijresmar.2021.09.008
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    Cited by:

    1. Hu, Peng & Gong, Yeming & Lu, Yaobin & Ding, Amy Wenxuan, 2023. "Speaking vs. listening? Balance conversation attributes of voice assistants for better voice marketing," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 109-127.
    2. Halbauer, Ingo & Jacob, Saskia & Klarmann, Martin, 2022. "Brand presentation order in voice shopping: Understanding the effects of sequential product presentation," Journal of Retailing, Elsevier, vol. 98(4), pages 759-778.

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