.png){kind=icon}
返回
Structural topic modelling segmentation: a segmentation method combining latent content and customer context
.png){kind=icon}
PRE
AI
DOI：10.1080/0267257X.2021.1880464.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Market segmentation
structural topic modelling
strategic marketing
social media
cluster analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
11
论文数:
1.4K
被引数:
4.6K
机构
引用论文
.png){kind=icon}
WHEN SOCIAL MEDIA DELIVERS CUSTOMER SERVICE: DIFFERENTIAL CUSTOMER TREATMENT IN THE AIRLINE INDUSTRY
PREAI
分享收藏
查看更多内容