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A review on customer segmentation methods for personalized customer targeting in e-commerce use cases

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  • Miguel Alves Gomes

    (University of Wuppertal)

  • Tobias Meisen

    (University of Wuppertal)

Abstract

The importance of customer-oriented marketing has increased for companies in recent decades. With the advent of one-customer strategies, especially in e-commerce, traditional mass marketing in this area is becoming increasingly obsolete as customer-specific targeting becomes realizable. Such a strategy makes it essential to develop an underlying understanding of the interests and motivations of the individual customer. One method frequently used for this purpose is segmentation, which has evolved steadily in recent years. The aim of this paper is to provide a structured overview of the different segmentation methods and their current state of the art. For this purpose, we conducted an extensive literature search in which 105 publications between the years 2000 and 2022 were identified that deal with the analysis of customer behavior using segmentation methods. Based on this paper corpus, we provide a comprehensive review of the used methods. In addition, we examine the applied methods for temporal trends and for their applicability to different data set dimensionalities. Based on this paper corpus, we identified a four-phase process consisting of information (data) collection, customer representation, customer analysis via segmentation and customer targeting. With respect to customer representation and customer analysis by segmentation, we provide a comprehensive overview of the methods used in these process steps. We also take a look at temporal trends and the applicability to different dataset dimensionalities. In summary, customer representation is mainly solved by manual feature selection or RFM analysis. The most commonly used segmentation method is k-means, regardless of the use case and the amount of data. It is interesting to note that it has been widely used in recent years.

Suggested Citation

  • Miguel Alves Gomes & Tobias Meisen, 2023. "A review on customer segmentation methods for personalized customer targeting in e-commerce use cases," Information Systems and e-Business Management, Springer, vol. 21(3), pages 527-570, September.
    • Handle: RePEc:spr:infsem:v:21:y:2023:i:3:d:10.1007_s10257-023-00640-4
    DOI: 10.1007/s10257-023-00640-4
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    Cited by:

    1. Adam Wasilewski & Anna Zgrzywa-Ziemak, 2024. "Multi-variant e-commerce user interfaces for business sustainability," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(4), pages 211-229.
    2. Chen, Qiuying & Choi, Beom-Jin & Lee, Sang-Joon, 2025. "Tailoring customer segmentation strategies for luxury brands in the NFT market – The case of SUPERGUCCI," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
    3. Konstantinos Zervoudakis & Stelios Tsafarakis, 2025. "Customer segmentation using flying fox optimization algorithm," Journal of Combinatorial Optimization, Springer, vol. 49(1), pages 1-20, January.
    4. Li, Yinan & Liu, Ying & Yu, Muran, 2025. "Consumer segmentation with large language models," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).

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