An unsupervised learning approach to basket type definition in FMCG sector based on household panel data

Yigit A. T., Kaya T., Dogruak U.

International Journal of Information and Decision Sciences, vol.14, no.3, pp.243-259, 2022 (Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1504/ijids.2022.125187
  • Journal Name: International Journal of Information and Decision Sciences
  • Journal Indexes: Scopus, PASCAL, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.243-259
  • Keywords: basket analysis, cluster analysis, consumer panel, deep learning, ensemble learning, fast-moving consumer goods, FMCG, K-means, supervised learning
  • Istanbul Technical University Affiliated: Yes

Abstract

© 2022 Inderscience Enterprises Ltd.The purpose of this study is to propose a clustering-based modelling approach to define the main groups of baskets in Turkish fast-moving consumer goods (FMCG) industry regarding the sectoral decomposition, the total value and the size of the baskets. To do this, based on the information regarding nearly three million basket purchases made in 2018 by more than 14,000 households, alternative unsupervised learning methods such as K-means, and Gaussian mixtures are implemented to obtain and define the basket patterns in Turkey. Additionally, a supervised ensemble learning approach based on XGBoost method is also selected among fully connected neural networks and random forest models to assign the new baskets into the existing clusters. Results show that, 'SaveTheDay', 'CareTrip', 'Breakfast', 'SuperMain' and 'MeatWalk' are among the most important basket types in Turkish FMCG sector.