Optimal Internet Media Selection

Authors:
Peter J. Danaher;Janghyuk Lee;Laoucine Kerbache
Affiliations:
Melbourne Business School, Carlton, Victoria 3053, Australia;Korea University Business School, An-am, Seong-buk, Seoul, South Korea;Department of OMIT and the Research Center GREGHEC, HEC School of Management, 78351 Paris, France
Venue:
Marketing Science
Year:
2010

Citing 7
Cited 4

Advertising on the Internet

Advertising on the Internet
New metrics for new media: toward the development of Web measurement standards

World Wide Web Journal - Special issue on advancing HTML: style and substance
Search Engine Advertising: Buying Your Way to the Top to Increase Sales

Search Engine Advertising: Buying Your Way to the Top to Increase Sales
Modeling Browsing Behavior at Multiple Websites

Marketing Science
Improvements to the Linear Programming Based Scheduling of Web Advertisements

Electronic Commerce Research
Optimal Scheduling and Placement of Internet Banner Advertisements

IEEE Transactions on Knowledge and Data Engineering
Modeling Page Views Across Multiple Websites with an Application to Internet Reach and Frequency Prediction

Marketing Science

Online keyword based advertising: Impact of ad impressions on own-channel and cross-channel click-through rates

Decision Support Systems
Ad Gist: Ad Communication in a Single Eye Fixation

Marketing Science
Optimizing direct response in Internet display advertising

Electronic Commerce Research and Applications
Optimal selection of media vehicles using customer databases

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.