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Authors
Affiliations
1 Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IN
Source
International Journal of Business Analytics and Intelligence, Vol 5, No 2 (2017), Pagination: 15-21
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
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