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Cited by 165Customer churn and segmentationConsumer Market Behavior and PricingCustomer Service Quality and Loyalty

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

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Alves Gomes, Miguel, Meisen, Tobias·2023·Information Systems and e-Business Management
DOI 10.1007/s10257-023-00640-4
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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.

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Fig. 1 Flow chart of the literature research process
Fig. 1 Flow chart of the literature research processCC-BY · From the PDF
Fig. 2 Process of customer targeting based on behavioral information gathered from data
Fig. 2 Process of customer targeting based on behavioral information gathered from dataCC-BY · From the PDF
Fig. 3 Distribution of surveyed publications from 2000 until 2022
Fig. 3 Distribution of surveyed publications from 2000 until 2022CC-BY · From the PDF
Fig. 4 Distribution of the surveyed feature selection methods over the years
Fig. 4 Distribution of the surveyed feature selection methods over the yearsCC-BY · From the PDF
Fig. 5 Distribution of the surveyed clustering methods over the years of publication
Fig. 5 Distribution of the surveyed clustering methods over the years of publicationCC-BY · From the PDF
Table 1 Use cases and utilized segmentation approaches for customer targeting of commerce business from the survey literature Segmentation Retail Bank Mobile operator video & music Book Sports Automobile Fashion Travel Food Electronic Manufacturing Other
Table 1 Use cases and utilized segmentation approaches for customer targeting of commerce business from the survey literature Segmentation Retail Bank Mobile operator video & music Book Sports Automobile Fashion Travel Food Electronic Manufacturing OtherCC-BY · From the PDF
Fig. 6 Distribution of the segmentation methods used evaluation methods
Fig. 6 Distribution of the segmentation methods used evaluation methodsCC-BY · From the PDF

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Metadata

Authors
Alves Gomes, M., & Meisen, T.
Affiliations
University of Wuppertal (DE)
ORCID
Venue
Information Systems and e-Business Management
Publisher
Springer Science and Business Media LLC
Published
2023-06-09
Volume
21
Issue
3
Pages
527-570
ISSN
  • 1617-9846 (ISSN-L)
  • 1617-9854
DOI
10.1007/s10257-023-00640-4
OpenAlex
W4380085684
Cited by
165
References
1
Citations by year
2026: 47 · 2025: 68 · 2024: 46 · 2023: 4
FWCI
43.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Information Systems and e-Business Management (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Econstor (Econstor) (submittedVersion, cc-by)
Funders
Bergische Universität Wuppertal
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
  • Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
Keywords
customer segmentation, e-commerce, customer-centric marketing, customer behavior analysis, RFM analysis, K-means clustering, feature selection, temporal trends

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