Marketing Science 
Volume 29, Issue 2, March-April 2010, Page 336 - 347 

  

---

Peter J. Danaher 
Melbourne Business School, Carlton, Victoria 3053, Australia 
Janghyuk Lee 
Korea University Business School, An-am, Seong-buk, Seoul, South Korea 
Laoucine Kerbache 
Department of OMIT and the Research Center GREGHEC, HEC School of Management, 78351 Paris, France 
http://dx.doi.org/10.1287/mksc.1090.0507 



Abstract 

In this study we develop a method that optimally selects online media vehicles and determines the number of advertising impressions that should be purchased and then served from each chosen website. As a starting point, we apply Danaher's [Danaher, P. J. 2007. Modeling page views across multiple websites with an application to Internet reach and frequency prediction. Marketing Sci. 26(3) 422–437] multivariate negative binomial distribution (MNBD) for predicting online media exposure distributions. The MNBD is used as a component in the broader task of media selection. Rather than simply adapting previous selection methods used in traditional media, we show that the Internet poses some unique challenges. Specifically, online banner ads and other forms of online advertising are sold by methods that differ substantially from the way other media advertising is sold. We use a nonlinear optimization algorithm to solve the optimization problem and derive the optimum online media schedule. Data from an online audience measurement firm and an advertising agency are used to illustrate the speed and accuracy of our method, which is substantially quicker than using complete enumeration. 

Keywords

advertising ; 
Internet marketing ; 
media ; 
optimization ; 
probability models