What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent than statistics-focused metrics. However, extant studies rarely use domain experts in evaluating the outcomes. Out of the 169 studies that provided details about hyperparameters, more than four out of five used segment size as their only hyperparameter. Typically, studies generate four segments, although the maximum number rarely exceeds twenty, and in most cases, is less than ten. Based on these findings, we propose seven key goals and three practical implications to enhance customer segmentation research and application.

选择什么算法进行客户细分？您应该使用一种算法还是多种算法？您应该创建多少个客户群？如何评价结果？在本研究中，我们进行了系统的文献综述，以解决客户细分研究和实践中的这些核心问题。从 172 篇相关文章中提取信息的结果表明，算法客户细分是客户细分的主要方法。我们发现研究人员采用 46 种不同的算法和 14 种不同的评估指标。对于算法来说，K-means 聚类是最常用的。对于指标而言，以分离为中心的指标比以统计为中心的指标更为普遍。然而，现有的研究很少聘请领域专家来评估结果。在提供有关超参数详细信息的 169 项研究中，超过五分之四使用段大小作为唯一的超参数。通常，研究会生成四个部分，但最大数量很少超过二十个，并且在大多数情况下少于十个。基于这些发现，我们提出了七个关键目标和三个实际意义，以加强客户细分研究和应用。