Proof running two state-of-the-art pattern recognition techniques in the field of direct marketing

Authors:
Stijn Viaene;Bart Baesens;Guido Dedene;Jan Vanthienen;Dirk Van den Poel
Affiliations:
Department of Applied Economic Sciences, K.U.Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Applied Economic Sciences, K.U.Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Applied Economic Sciences, K.U.Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Applied Economic Sciences, K.U.Leuven, Naamsestraat 69, B-3000 Leuven, Belgium;Department of Marketing, Ghent University, Hoveniersberg 24, B-9000 Ghent, Belgium
Venue:
Enterprise information systems IV
Year:
2003

Citing 8
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Hybrid neural network models for bankruptcy predictions

Decision Support Systems
Using neural networks to aid the diagnosis of breast implant rupture

Computers and Operations Research
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Robust Classification for Imprecise Environments

Machine Learning
Measuring lift quality in database marketing

ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Mastering Data Mining: The Art and Science of Customer Relationship Management

Mastering Data Mining: The Art and Science of Customer Relationship Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we synthesize the main findings of three repeat purchase modelling case studies using real-life direct marketing data. Historically, direct marketing -- more recently, targeted web marketing -- has been one of the most popular domains for the exploration of the feasibility and the viable use of novel business intelligence techniques. Many a data mining technique has been field tested in the direct marketing domain. This can be explained by the (relatively) low-cost availability of recency, frequency, monetary (RFM) and several other customer relationship data, the (relatively) well-developed understanding of the task and the domain, the clearly identifiable costs and benefits, and because the results can often be readily applied to obtain a high return on investment. The purchase incidence modelling cases reported on in this paper were in the first place undertaken to trial run state-of-the-art supervised Bayesian learning multilayer perceptron (MLP) and least squares support vector machine (LS-SVM) classifiers. For each of the cases, we also aimed at exploring the explanatory power (relevance) of the available RFM and other customer relationship related variable operationalizations for predicting purchase incidence in the context of direct marketing.