How to support customer segmentation with useful cluster descriptions
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Author (Corporation)
Publication date
2015
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Advances in data mining. Applications and theoretical aspects
Special issue
DOI of the original publication
Link
Series
Lecture Notes in Artificial Intelligence
Series number
9165
Volume
Issue / Number
Pages / Duration
17-31
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
Cham
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
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.
Keywords
Event
15th Industrial Conference, ICDM 2015
Exhibition start date
Exhibition end date
Conference start date
11.07.2015
Conference end date
24.07.2015
Date of the last check
ISBN
978-3-319-20910-4
978-3-319-20909-8
10.1007/978-3-319-20910-4_2
978-3-319-20909-8
10.1007/978-3-319-20910-4_2
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Witschel, H. F., Loo, S., & Riesen, K. (2015). How to support customer segmentation with useful cluster descriptions. In P. Perner (Ed.), Advances in data mining. Applications and theoretical aspects (pp. 17–31). Springer. https://doi.org/10.26041/fhnw-58