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Activity Number: 240
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Marketing
Abstract - #305773
Title: A SVM Approach to Netlift Modeling
Author(s): Ruiwen Zhang*+ and Taiyeong Lee
Companies: SAS Institute and SAS Institute
Address: 600 Research Dr, Cary, NC, 27513, United States
Keywords: netlift modeling ; incremental response ; SVM ; direct marketing

How do you know if a direct marketing target the right customers? This question becomes more important as commercial and nonprofit marketers spend more money on direct campaigns through mobile messaging, email, interactive consumer websites, social media, and promotional letters. Some customers may not react positively to a particular marketing intervention, while others may even react negatively. Conventional "response" models target on all the people who are likely to buy, thus wasting the cost on the group of customers who would have bought anyway. Netlift modeling techniques which directly model the incremental impact of incentive treatments and optimize ROI have been popularly used recent years. SVM (support vector machine), which provides accurate classifier and also worries less about overfitting through the regulation parameter, makes it an ideal methodology to be applied in the netlift modeling. In this paper, we propose a SVM approach to estimate and predict the incremental response, and so distinguish the optimal customers who are more likely to respond only when they are targeted.

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