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

Ma, R, Mao, D, Cao, D ORCID: https://orcid.org/0000-0002-2614-3726, Luo, S, Gupta, S and Wang, Y, 2024. From vineyard to table: uncovering wine quality for sales management through machine learning. Journal of Business Research, 176: 114576. ISSN 0148-2963

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Abstract

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.

Item Type:	Journal article
Publication Title:	Journal of Business Research
Creators:	Ma, R., Mao, D., Cao, D., Luo, S., Gupta, S. and Wang, Y.
Publisher:	Elsevier
Date:	April 2024
Volume:	176
ISSN:	0148-2963
Identifiers:	Number Type 10.1016/j.jbusres.2024.114576 DOI S0148296324000808 Publisher Item Identifier 1867138 Other
Divisions:	Schools > Nottingham Business School
Record created by:	Jonathan Gallacher
Date Added:	13 Mar 2024 10:44
Last Modified:	13 Mar 2024 10:44
URI:	https://irep.ntu.ac.uk/id/eprint/51068

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