How to Support Customer Segmentation with Useful Cluster Descriptions

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• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9165
• 期：1
• 页码：17-31
• 全文大小：2,088 KB
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• 作者单位：Hans Friedrich Witschel (5)
  Simon Loo (5)
  Kaspar Riesen (5)
  
  5. University of Applied Sciences Northwestern Switzerland (FHNW), Riggenbachstrasse 16, 4600, Olten, Switzerland
  
• 丛书名：Advances in Data Mining: Applications and Theoretical Aspects
• ISBN：978-3-319-20910-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Customer or market segmentation is an important instrument for the optimisation of marketing strategies and product portfolios. Clustering is a popular data mining technique used to support such segmentation -it groups customers into segments that share certain demographic or behavioural characteristics. In this research, we explore several automatic approaches which support an important task that starts after the actual clustering, namely capturing and labeling the “essence-of segments. We conducted an empirical study by implementing several of these approaches, applying them to a data set of customer representations and studying the way our study participants interacted with the resulting cluster representations. Major goal of the present paper is to find out which approaches exhibit the greatest ease of understanding on the one hand and which of them lead to the most correct interpretation of cluster essence on the other hand. Our results indicate that using a learned decision tree model as a cluster representation provides both good ease of understanding and correctness of drawn conclusions.