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A Comparative Analysis of Support Vector Machines & Logistic Regression for Propensity Based Response Modeling


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

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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.
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  • Athanassopoulos, A. D. (2000). Customer satisfaction cues to support market segmentation and explain switching behavior. Journal of Business Research, 47, 191–207.

  • Burges, C.J.C. (1998). A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121–167.

  • Bennett, K. P., & Campbell, C. (2000). Support vector machines: hype or hallelujah? SIGKDD Explor. Newsl. 2, 1–13

  • Cui, D., & Curry, D. (2005). Prediction in marketing using the support vector machine. Marketing Science, 24, 595–615.

  • Cui, G., Wong, M. L., & Lui, H. K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52(4), 597–612.

  • Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines, Software Retrieved from http://www.csie.ntu.edu.tw/~cjlin/libsvm.

  • Elsner, R., Krafft, M., & Huchzermeier, A. (2004). Optimizing Rhenania’s Direct Marketing Business through dynamic Multilevel Modeling (DMLM) in a Multicatalog-Brand Environment. Marketing Science, 23(2) 192-206.

  • Hosmer, D.W., & Lemeshow, S. (1989). Applied logistic regression, New York: John Wiley & Sons, Inc

  • 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, Guimaraes, Portugal, October, 2011. EUROSIS.

  • Ngai, E., Xiu, L., & Chau, D. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst Appl, 36, 2592–2602. doi:10.1016/j.eswa.2008.02.021

  • Kim, D., Lee, H., & Cho, S. (2008). Response modelling with support vector regression. Expert Systems with Applications, 34, 1102–1108.

  • Hsu, C., Chang, C., & Lin, C. (2010). A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University.

  • Viaene, S., Baesens, B., Van Gestel, T., Suykens, J., Van den Poel, D., Vanthienen, J., De Moor, B., & Dedene, G. (2001). Knowledge discovery in a direct marketing case using least squares support vector machines. International Journal of Intelligent System, 16, 1023–1036

  • Vapnik, V. (2000). The Nature of Statistical Learning Theory, Springer, New York.

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The Elements of Statistical Learning. Springer, 2001.

  • Schhiffman J. B., & Kanuk, L. L. (1997). Consumer Behavior published by Prentice Hall Sixth edition, 446.

  • Shin, H. J., & Cho, S. (2006). Response modeling with support vector machine. Expert Systems with Application, 30(4), 746-760.

  • Williams, G. J. (2011). Data mining with Rattle and R: The art of excavating data for knowledge discovery, Use R!, Springer.


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  • A Comparative Analysis of Support Vector Machines & Logistic Regression for Propensity Based Response Modeling

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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


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