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Activity Number: 81
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Marketing
Abstract #313281 View Presentation
Title: Optimal Internet Media Selection Using General Loss Functions
Author(s): Courtney Paulson*+ and Gareth James and Lan Luo
Companies: University of Southern California and University of Southern California and University of Southern California
Keywords: Internet marketing ; budget allocation ; penalized regression ; demand function ; generalized lasso ; linear constraints
Abstract:

Current methods for optimizing advertising budgets rely heavily on identifying a particular subset of advertising opportunities. However, in the case of Internet advertising, this is infeasible; the set of opportunities is limited only by the sheer number of websites. Even further, these websites often vary significantly by site traffic, advertising costs, and correlations in site visits. To address such challenges, we formulate a procedure for automatic subset and budget optimization over a very large set of Internet websites. Due to the unique nature of this problem, we optimize over a very versatile general loss function and develop an efficient algorithm for computing our optimization over a grid of tuning parameters. Furthermore, while existing methods can only handle optimal Internet selection problems on the order of 10 websites, we propose a new approach that works well for high-dimensional problems. We also demonstrate this increased dimensionality does not diminish the algorithm's efficiency. While our method performs similarly to existing methods for small numbers of websites, the proposed method can further handle budget allocation across very large numbers (e.g. 500).


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