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Prasad, K. V. N. K.
- A Comparative Analysis of Support Vector Machines & Logistic Regression for Propensity Based Response Modeling
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Affiliations
1 Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IN
1 Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IN
Source
International Journal of Business Analytics and Intelligence, Vol 3, No 1 (2015), Pagination: 7-16Abstract
Increasing cost of soliciting customers along with amplified efforts to improve the bottom-line amidst intense competition is driving the firms to rely on more cutting edge analytic methods by leveraging the knowledge of customer-base that is allowing the firms to engage better with customers by offering right product/service to right customer. Increased interest of the firms to engage better with their customers has evidently resulted into seeking answers to the key question: Why are customers likely to respond? in contrast to just seek answers for question: Who are likely to respond?This has resulted in developing propensity based response models that have become a center stage of marketing across customer life cycle. Propensity based response models are used to predict the probability of a customer or prospect responding to some offer or solicitation and also explain the drivers - why the customers are likely to respond. The output from these models will be used to segment markets, to design strategies, and to measure marketing performance.
In our present paper we will use support vector machines and Logistic Regression to build propensity based response models and evaluate their performance.
Keywords
Response Modeling, Propensity, Logistic Regression, Support Vector Machines.
References
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- Application of Generalised Additive Logistic Model for Targeted Marketing
Abstract Views :310 |
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Authors
Affiliations
1 Department of Statistics, Acharya Nagarjuna University, Guntur, Andhra Pradesh, IN
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: 15-21Abstract
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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