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Applying machine learning to market analysis: Knowing your luxury consumer

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  • Kuo Chi-Hsien
  • Shinya Nagasawa

Abstract

Chinese consumer research in the luxury sector is the emphasis in the business research field. However, it can be cost-intensive or time-consuming to interpret big data from any research conducted in the field. In this paper, the researchers created a machine-learning model to help minimize those research barriers.This study analyzed Chinese luxury consumption behavior, while the Chinese contributed 33% of the global luxury market in 2018 and play as a growth engine in the luxury market (Bain & Company. 2019. https://www.bain.com/insights/whats-powering-chinas-market-for-luxury-goods/). The researchers interpreted this analysis using machine-learning algorithms through different sets of conditions and then proposed an understandable and highly accurate machine-learning model.Unlike traditional statistical methods, which rely on domain experts to create hand-crafted features, this paper proposes an unsupervised end-to-end model that can directly and accurately process questionnaire data without human intervention. This paper also demonstrates how to practically apply an automatic unsupervised analysis method (PCA) to find inferences in the big data, and helps interpret the implied meaning to the questions.

Suggested Citation

  • Kuo Chi-Hsien & Shinya Nagasawa, 2019. "Applying machine learning to market analysis: Knowing your luxury consumer," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(4), pages 404-419, October.
    • Handle: RePEc:taf:tjmaxx:v:6:y:2019:i:4:p:404-419
    DOI: 10.1080/23270012.2019.1692254
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    Cited by:

    1. Ting Hou & Baihua Cheng & Rongxiao Wang & Wei Xue & Peggy E. Chaudhry, 2020. "Developing Industry 4.0 with systems perspectives," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 741-748, July.
    2. Meifang Yao & Dan Ye & Liyi Zhao, 2022. "The relationship between inbound open innovation and the innovative use of information technology by individuals in teams of start‐ups," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 503-515, May.
    3. Fang Wang, 2022. "AI‐enabled IT capability and organizational performance," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 609-617, May.
    4. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    5. Shida Rastegari Henneberry & Riza Radmehr, 2020. "Quantifying impacts of internships in an international agriculture degree program," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-28, August.
    6. Hong Jiang & Jinlong Gai & Shukuan Zhao & Peggy E. Chaudhry & Sohail S. Chaudhry, 2022. "Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 361-378, May.

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