Applying machine learning to market analysis: Knowing your luxury consumer

Kuo Chi-Hsien, Shinya Nagasawa

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)404-419
Number of pages16
JournalJournal of Management Analytics
Volume6
Issue number4
DOIs
Publication statusPublished - 2019 Oct 2

Keywords

  • Luxury consumer
  • end-to-end model
  • machine learning
  • principal component analysis (PCA)
  • unsupervised learning

ASJC Scopus subject areas

  • Statistics and Probability
  • Business, Management and Accounting (miscellaneous)
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Applying machine learning to market analysis: Knowing your luxury consumer'. Together they form a unique fingerprint.

Cite this