Abstract
The purpose of this study is to suggest a clustering approach to define the main groups of baskets in Turkish fast-moving consumer goods (FMCG) industry based on the sectoral decomposition, the total value and the size of the baskets. To do this, based on the information regarding the 2,965,837 baskets (8,147,233 transactions) of 14293 households which took place in the calendar year 2018, alternative unsupervised learning methods such as K-means, and Gaussian mixtures are implemented to obtain and define the basket patterns in Turkey. A supervised ensembling approach based on XG-Boost method is also suggested 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.
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Acknowledgement
This study is an outcome of the collaboration project conducted by IPSOS and ITUNOVA (2019) entitled “Clustering FMCG Consumers and Shopping Baskets in Turkey using Basket Size, Product Decomposition and Household Profiles”.
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Kaya, T., Yiğit, A.T., Doğruak, U. (2021). Basket Patterns in Turkey: A Clustering of FMCG Baskets Using Consumer Panel Data. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (eds) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197. Springer, Cham. https://doi.org/10.1007/978-3-030-51156-2_10
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