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.

近几十年来，以客户为导向的营销对于公司来说越来越重要。随着单一客户策略的出现，尤其是在电子商务领域，随着特定客户定位的实现，这一领域的传统大众营销正变得越来越过时。这种策略使得对个人客户的兴趣和动机有一个基本的了解变得至关重要。为此目的经常使用的一种方法是分段，该方法近年来稳步发展。本文的目的是提供不同分割方法及其当前技术水平的结构化概述。为此，我们进行了广泛的文献检索，确定了 2000 年至 2022 年期间的 105 篇出版物，这些出版物涉及使用细分方法分析客户行为。基于本文的语料库，我们对所使用的方法进行了全面的回顾。此外，我们还研究了时间趋势的应用方法及其对不同数据集维度的适用性。基于本文语料库，我们确定了一个由信息（数据）收集、客户代表、通过细分进行的客户分析和客户定位组成的四个阶段的过程。关于客户代表和客户细分分析，我们全面概述了这些流程步骤中使用的方法。我们还研究了时间趋势以及对不同数据集维度的适用性。综上所述，客户表示主要通过手动特征选择或RFM分析来解决。最常用的分割方法是 k 均值，无论用例和数据量如何。有趣的是，它近年来得到了广泛的应用。