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Title: A dynamic understanding of customer behavior processes based on clustering and sequence mining
Authors: Seret, Alex # ×
vanden Broucke, Seppe
Baesens, Bart #
Vanthienen, Jan #
Issue Date: 2014
Publisher: Elsevier
Series Title: Expert Systems with Applications vol:41 issue:10 pages:4648-4657
Abstract: In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which — when combined — represent the “behavior process” as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.
ISSN: 0957-4174
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
× corresponding author
# (joint) last author

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