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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  • 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