WHAT DRIVES HIGHER BEER RATINGS? EVIDENCE FROM BIG DATA

Andrew Manikas, Michael Godfrey and Jason Woldt

International Journal of Management and Marketing Research, 2022, vol. 15, issue 1, 1-13

Abstract: The purpose of this paper is to present a data analytics model utilizing a large database of beer reviews. We are witnessing a big data revolution with companies’ ability to capture and analyze large volumes, velocity, and variety of consumer spending patterns and purchase factors. Analysis of consumer reviews recently has shifted from focusing only on consumer ratings to also mining the content of those reviews. Using a sample of approximately 9 million reviews over 22 years from BeerAdvocate, we developed a multiple regression model to test the relationship of alcohol by volume (ABV) and review word length to the rating that a beer received. Our main finding was that the more (ABV) that a beer had, the higher was that beer’s average rating. Although increased ABV was associated with higher consumer scores, we found diminishing returns. We also confirmed that negativity bias occurred in these reviews—lower-scoring beers tended to have longer reviews than higher scoring beers had. This suggests that breweries would be advised to have higher alcohol content in their products to meet consumer preferences (even if those preferences are subconscious). Given the supply chain disruptions plaguing the world today, big data analysis of consumer preferences for beer could enable both manufacturers with constrained capacity to better align product offerings with consumer tastes and preferences.

Keywords: L31; L84; M11 (search for similar items in EconPapers)
Date: 2022
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