ABSTRACT

This research introduces a method for segmenting customers using Structural Topic Modelling (STM), a text analysis tool capable of capturing topical content and topical prevalence differences across customers while incorporating metadata. This approach is particularly suitable for contexts in which textual data is either a critical component or is the only data available for segmentation. The ability to incorporate metadata by using STM provides better clustering solutions and supports richer segment profiles than can be produced with typical topic modelling approaches. We empirically illustrate the application of this method in two contexts: 1) a context in which related metadata is readily available; and 2) a context in which metadata is virtually non-existent. The second context exemplifies how ad-hoc generated metadata can increase the utility of the method for identifying distinct segments.

摘要

本研究介绍了一种使用结构主题建模 (STM) 细分客户的方法，STM 是一种文本分析工具，能够在合并元数据的同时捕获客户之间的主题内容和主题流行度差异。这种方法特别适用于文本数据是关键组成部分或者是唯一可用于分割的数据的上下文。使用 STM 合并元数据的能力提供了更好的聚类解决方案，并支持比使用典型主题建模方法生成的更丰富的细分配置文件。我们凭经验说明了这种方法在两种情况下的应用：1）相关元数据很容易获得的情况；2) 元数据实际上不存在的上下文。