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From vineyard to table: Uncovering wine quality for sales management through machine learning

Lookup NU author(s): Professor Suraksha Gupta

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Abstract

© 2024 Elsevier Inc.The literature currently offers limited guidance for retailers on how to use analytics to decipher the relationship between product attributes and quality ratings. Addressing this gap, our study introduces an advanced ensemble learning approach to develop a nuanced framework for assessing product quality. We validated the effectiveness of our framework with a dataset comprising 1,599 red wine samples from Portugal's Minho region. Our findings show that this model surpasses previous ones in accurately predicting product quality, presenting retailers with a sophisticated tool to transform product data into actionable insights for sales management. Furthermore, our approach yields significant benefits for researchers by identifying latent attributes in extensive data collections, which can inform a deeper understanding of consumer preferences and guide the strategic planning of marketing promotions.

Publication metadata

Author(s): Ma R, Mao D, Cao D, Luo S, Gupta S, Wang Y

Publication type: Article

Publication status: Published

Journal: Journal of Business Research

Year: 2024

Volume: 176

Print publication date: 01/04/2024

Online publication date: 24/02/2024

Acceptance date: 10/02/2024

ISSN (print): 0148-2963

ISSN (electronic): 1873-7978

Publisher: Elsevier Inc.

URL: https://doi.org/10.1016/j.jbusres.2024.114576

DOI: 10.1016/j.jbusres.2024.114576

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