Review valence impact on jello shot sales

James Christopher Westland ()
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James Christopher Westland: University of Illinois Chicago

Journal of Marketing Analytics, 2025, vol. 13, issue 3, No 8, 697-708

Abstract: Abstract This study examines customer review impact on jello shot sales, leveraging a natural experiment from production changes in the U.S. beverage market. Analyzing over 15,000 reviews using Natural Language Processing (NLP) across NRC Emotion, Bing, and AFINN lexicons, the research identifies significant correlations between review valence and sales. Specific sentiments, particularly anger, disgust, joy, and trust, emerge as key sales predictors, with negative emotions exerting a disproportionately strong influence. While star ratings correlate positively with sales, platform biases render them less reliable predictors than nuanced sentiment. Generalized linear modeling confirms review sentiment affects sales magnitude and direction. Findings advance literature on review valence (Chevalier and Mayzlin in J Market Res 43(3):345–354, 2006) and negativity bias (Baumeister et al. in Rev Gen Psychol 5(4):323–370, 2001), highlighting the need for granular sentiment analysis beyond aggregate ratings (Mudambi and Schuff in MIS Q 34(1):185–200, 2010a). Methodologically, it demonstrates NLP’s utility in linking sentiment to sales outcomes (Cui et al. in Int J Electron Commer 17(1):39–58, 2008). For practitioners, results advocate for sentiment monitoring to inform customer-centric strategies. For academics, it provides empirical grounding for consumer behavior and eWOM theories, considering platform effects.

Keywords: Consumer sentiment; Valence; Natural language processing; Electronic commerce; Consumer reviews (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1057/s41270-025-00412-8

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