We develop a framework to reduce the number of customer segments to the smallest quantity without losing essential information of the underlying population in the electronic marketplace. As a use case of this approach, we create personas for these segments to enhance customer understanding. We use (a) matrix factorization to identify customer behaviors and construct customer segments, (b) statistical heuristics to collapse into meaningful segments, and (c) automation to enrich by generating a persona profile for each segment. We evaluate our approach in a case study using more than 21 million online flight bookings of a major airline company resulting in a 57.5% decrease from 1194 to 507 segments, thereby reducing segment noise. Three customer mega-segments emerge: Behaviorally same – Demographically different, Behaviorally different - Demographically same, and Behaviorally and Demographically different. As one of the first efforts at the essential task of customer segmentation reduction for large customer populations, findings have implications for organizations desiring to employ segmentation and/or personas for enhanced customer understanding.

我们开发了一个框架，以将客户细分的数量减少到最小数量，而不会丢失电子市场中潜在人群的基本信息。作为这种方法的一个用例，我们为这些细分市场创建角色以增强客户理解。我们使用 (a) 矩阵分解来识别客户行为并构建客户细分，(b) 统计启发式分解为有意义的细分，以及 (c) 通过为每个细分生成角色档案来实现自动化。我们在一个案例研究中评估了我们的方法，使用了一家大型航空公司的超过 2100 万次在线航班预订，导致从 1194 个细分市场减少到 507 个细分市场，从而降低了细分市场噪音。出现了三个大客户群体：行为相同 – 人口统计学不同,行为不同 - 人口统计学相同，以及行为和人口统计学不同。作为减少大量客户群的客户细分这一基本任务的首要工作之一，研究结果对希望采用细分和/或角色来增强客户理解的组织具有影响。