Strategic Online Advertising: Modeling Internet User Behavior with Advertising.com

We investigated how online advertising could be made more receptive to Internet users´ needs, thereby improving the efficacy. Only three to five viewers in a thousand click on a given online banner advertisement. To improve the low response rate, marketers need to reach the right audience, which will yield a higher return on advertising dollars by eliminating wasted ads and maximizing campaign effectiveness. Users see relevant ads based on their preferences and fewer ads that do not interest them. We identified and modeled user behavior and characterized a subpopulation of users to help predict advertising response. We then developed an exploratory optimization and targeting technology for use by advertising.com by serving users ads probabilistically on the basis of their online behaviors. After preliminary algorithm validation, the increase in early user clicks indicates the potential effectiveness of these models in improving the online advertising response rate. The resultant potential increase in action could lead to increased revenue