International Journal of Business Analytics and Intelligence

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Application of Generalised Additive Logistic Model for Targeted Marketing


Affiliations
1 Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
     

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This study focuses on how to support marketing decision makers better in identifying better prospective customers by using generalised additive models (GAMs). Compared to logistic regression, GAM relaxes the linearity constraint which allows for complex non-linear fits to the data. In this paper, we examine how GAM-based logistic models perform compared to traditional logistic regression model and also provide some implications.

Keywords

Additive Logistics Model, Targeted Marketing.
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  • Craven, P., & Wahba, G. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized crossvalidation Number. Math., 31, 377-403.

  • Hastie, T. J., & Tibshirani, R. J. (1990). Generalized additive models. New York: Chapman & Hall.

  • Liu, W., & Cela, J. (2007). Improving credit scoring by generalized additive model. SAS global forum 2007 (paper 078-2007).

  • McCullagh, P., & Nelder, J. (1989). Generalized linear models. Chapman and Hall.

  • Moro, S., Laureano, R., & Cortez, P. (2011). Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM’2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.

  • Muller, M. (2000). Semi-parametric extensions to generalized linear models. Habilitationsschrift.

  • Wood, S. N. (2006). Generalized additive model: An introduction with R. Chapman and Hall/CRC.

  • Stone, C. J. (1985). Additive regression and other nonparametric models. Annals of Statistics, 13, 689-705.

  • http://support.sas.com/kb/32/927.html


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  • Application of Generalised Additive Logistic Model for Targeted Marketing

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Authors

K. V. N. K. Prasad
Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India
G.V.S.R. Anjaneyulu
Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

Abstract


This study focuses on how to support marketing decision makers better in identifying better prospective customers by using generalised additive models (GAMs). Compared to logistic regression, GAM relaxes the linearity constraint which allows for complex non-linear fits to the data. In this paper, we examine how GAM-based logistic models perform compared to traditional logistic regression model and also provide some implications.

Keywords


Additive Logistics Model, Targeted Marketing.

References