Improving direct mail targeting through customer response modeling

Coussement, Kristof; Harrigan, Paul; Benoit, Dries

Document type :

Article dans une revue scientifique

DOI :

10.1016/j.eswa.2015.06.054

Title :

Improving direct mail targeting through customer response modeling

Author(s) :

Coussement, Kristof [Auteur]
Lille économie management - UMR 9221 [LEM]
Harrigan, Paul [Auteur]
Benoit, Dries [Auteur]

Journal title :

Expert Systems with Applications

Pages :

8403-8412

Publisher :

Elsevier

Publication date :

2015-12

ISSN :

0957-4174

English keyword(s) :

Direct marketing
Direct mail
Response modeling
Database marketing

HAL domain(s) :

Sciences de l'Homme et Société
Sciences de l'Homme et Société/Gestion et management

English abstract : [en]

Direct marketing is an important tool in the promotion mix of companies, amongst which direct mailing is crucial. One approach to improve direct mail targeting is response modeling, i.e. a predictive modeling approach that ...
Show more >Direct marketing is an important tool in the promotion mix of companies, amongst which direct mailing is crucial. One approach to improve direct mail targeting is response modeling, i.e. a predictive modeling approach that assigns future response probabilities to customers based on their history with the company. The contributions to the response modeling literature are three-fold. First, we introduce well-known statistical and data-mining classification techniques (logistic regression, linear and quadratic discriminant analysis, naïve Bayes, neural networks, decision trees, including CHAID, CART and C4.5, and the k-NN algorithm) to the direct marketing community. Second, we run a predictive benchmarking study using the above classifiers on four real-life direct marketing datasets. The 10-fold cross-validated area under the receiver operating characteristics curve is used as evaluation metric. Third, we give managerial insights that facilitate the classifier choice based on the trade-off between interpretability and predictive performance of the classifier. The findings of the benchmark study show that data-mining algorithms (CHAID, CART and neural networks) perform well on this test bed, followed by simplistic statistical classifiers like logistic regression and linear discriminant analysis. It is shown that quadratic discriminant analysis, naïve Bayes, C4.5 and the k-NN algorithm yield poor performance.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL

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