Publication
Title
Loyal to your city? A data mining analysis of a public service loyalty program
Author
Abstract
Customer loyalty programs are largely present in the private sector and have been elaborately studied. Applications from the private sector have found resonance in a public setting, however, simply extrapolating research results is not acceptable, as their rationale inherently differs. This study focuses on data from a loyalty program issued by the city of Antwerp (Belgium). The aim of the loyalty card entails large citizen participation, however, an active user base of only 20 % is reached. Predictive techniques are employed to increase this number. Using spatial behavioral user information, a Naive Bayes classifier and a Support Vector Machine are used which result in models capable of predicting whether a user will actively use its card, whether a user will defect in the near future and which locations a user will visit. Also, a projection of spatial behavioral data onto even more fine-grained spatio-temporal data is performed. The results are promising: the best model achieves an AUC value of 92.5 %, 85.5 % and 88.12 % (averaged over five locations) for the predictions, respectively. Moreover, as behavior is modeled in more detail, better predictions are made. Two main contributions are made in this study. First, as a theoretical contribution, fine-grained behavioral data contributes to a more sound decision-making process. Second, as a practical contribution, the city of Antwerp can now make tailored strategic decisions to increase its active user base.
Language
English
Source (journal)
Decision support systems. - Amsterdam
Publication
Amsterdam : 2015
ISSN
0167-9236
Volume/pages
73(2015), p. 74-84
ISI
000353599200007
Full text (Publisher's DOI)
Full text (open access)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
[E?say:metaLocaldata.cgzprojectinf]
Big Data Mining for Customer Analytics.
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.04.2015
Last edited 19.11.2017
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