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

Author

Listed:
  • Ma, Rui
  • Mao, Di
  • Cao, Dongmei
  • Luo, Shuai
  • Gupta, Suraksha
  • Wang, Yichuan

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.

Suggested Citation

  • Ma, Rui & Mao, Di & Cao, Dongmei & Luo, Shuai & Gupta, Suraksha & Wang, Yichuan, 2024. "From vineyard to table: Uncovering wine quality for sales management through machine learning," Journal of Business Research, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:jbrese:v:176:y:2024:i:c:s0148296324000808
    DOI: 10.1016/j.jbusres.2024.114576
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