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Modeling complex longitudinal consumer behavior with dynamic Bayesian networks: an acquisition pattern analysis application

Anita Prinzie UGent and Dirk Van den Poel UGent (2011) JOURNAL OF INTELLIGENT INFORMATION SYSTEMS. 36(3). p.283-304
abstract
Longitudinal consumer behavior has been modeled by sequence analysis. A popular application involves Acquisition Pattern Analysis exploiting typical acquisition patterns to predict a customer's next purchase. Typically, the acquisition process is represented by an extensional, unidimensional sequence taking values from a symbolic alphabet. Given complex product structures, the extensional state representation rapidly evokes the state-space explosion problem. Consequently, most authors simplify the decision problem to the prediction of acquisitions for selected products or within product categories. This paper advocates the use of intensional state definitions representing the state by a set of variables thereby exploiting structure and allowing to model complex, possibly coupled sequential phenomena. The advantages of this intensional state space representation are demonstrated on a financial-services cross-sell application. A Dynamic Bayesian Network (DBN) models longitudinal customer behavior as represented by acquisition, product ownership and covariate variables. The DBN provides insight in the longitudinal interaction between a household's portfolio maintenance behavior and acquisition behavior. Moreover, it exhibits adequate predictive performance to support the financial-services provider's cross-sell strategy comparable to decision trees but superior to MulltiLayer Perceptron neural networks.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
FINANCIAL PRODUCTS, SEQUENTIAL INFORMATION, DURABLE GOODS, SERVICES, MARKOV, TIME, State space representation, Longitudinal, Sequence analysis, Acquisition pattern analysis, Cross-sell, Analytical customer relationship management
journal title
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
J. Intell. Inf. Syst.
volume
36
issue
3
pages
283 - 304
Web of Science type
Article
Web of Science id
000290037600003
JCR category
COMPUTER SCIENCE, INFORMATION SYSTEMS
JCR impact factor
0.618 (2011)
JCR rank
95/133 (2011)
JCR quartile
3 (2011)
ISSN
0925-9902
DOI
10.1007/s10844-009-0106-7
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1230618
handle
http://hdl.handle.net/1854/LU-1230618
date created
2011-05-23 14:55:07
date last changed
2011-05-24 09:22:46
@article{1230618,
  abstract     = {Longitudinal consumer behavior has been modeled by sequence analysis. A popular application involves Acquisition Pattern Analysis exploiting typical acquisition patterns to predict a customer's next purchase. Typically, the acquisition process is represented by an extensional, unidimensional sequence taking values from a symbolic alphabet. Given complex product structures, the extensional state representation rapidly evokes the state-space explosion problem. Consequently, most authors simplify the decision problem to the prediction of acquisitions for selected products or within product categories. This paper advocates the use of intensional state definitions representing the state by a set of variables thereby exploiting structure and allowing to model complex, possibly coupled sequential phenomena. The advantages of this intensional state space representation are demonstrated on a financial-services cross-sell application. A Dynamic Bayesian Network (DBN) models longitudinal customer behavior as represented by acquisition, product ownership and covariate variables. The DBN provides insight in the longitudinal interaction between a household's portfolio maintenance behavior and acquisition behavior. Moreover, it exhibits adequate predictive performance to support the financial-services provider's cross-sell strategy comparable to decision trees but superior to MulltiLayer Perceptron neural networks.},
  author       = {Prinzie, Anita and Van den Poel, Dirk},
  issn         = {0925-9902},
  journal      = {JOURNAL OF INTELLIGENT INFORMATION SYSTEMS},
  keyword      = {FINANCIAL PRODUCTS,SEQUENTIAL INFORMATION,DURABLE GOODS,SERVICES,MARKOV,TIME,State space representation,Longitudinal,Sequence analysis,Acquisition pattern analysis,Cross-sell,Analytical customer relationship management},
  language     = {eng},
  number       = {3},
  pages        = {283--304},
  title        = {Modeling complex longitudinal consumer behavior with dynamic Bayesian networks: an acquisition pattern analysis application},
  url          = {http://dx.doi.org/10.1007/s10844-009-0106-7},
  volume       = {36},
  year         = {2011},
}

Chicago
Prinzie, Anita, and Dirk Van den Poel. 2011. “Modeling Complex Longitudinal Consumer Behavior with Dynamic Bayesian Networks: An Acquisition Pattern Analysis Application.” Journal of Intelligent Information Systems 36 (3): 283–304.
APA
Prinzie, A., & Van den Poel, D. (2011). Modeling complex longitudinal consumer behavior with dynamic Bayesian networks: an acquisition pattern analysis application. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 36(3), 283–304.
Vancouver
1.
Prinzie A, Van den Poel D. Modeling complex longitudinal consumer behavior with dynamic Bayesian networks: an acquisition pattern analysis application. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS. 2011;36(3):283–304.
MLA
Prinzie, Anita, and Dirk Van den Poel. “Modeling Complex Longitudinal Consumer Behavior with Dynamic Bayesian Networks: An Acquisition Pattern Analysis Application.” JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 36.3 (2011): 283–304. Print.