Finding the right consumer: optimizing for conversion in display advertising campaigns

The ultimate goal of advertisers are conversions representing desired user actions on the advertisers' websites in the form of purchases and product information request. In this paper we address the problem of finding the right audience for display campaigns by finding the users that are most likely to convert. This challenging problem is at the heart of display campaign optimization and has to deal with several issues such as very small percentage of converters in the general population, high-dimensional representation of the user profiles, large churning rate of users and advertisers. To overcome these difficulties, in our approach we use two sources of information: a seed set of users that have converted for a campaign in the past; and a description of the campaign based on the advertiser's website. We explore the importance of the information provided by each of these two sources in a principled manner and then combine them to propose models for predicting converters. In particular, we show how seed set can be used to capture the campaign-specific targeting constraints, while the campaign metadata allows to share targeting knowledge across campaigns. We give methods for learning these models and perform experiments on real-world advertising campaigns. Our findings show that the seed set and the campaign metadata are complimentary to each other and both sources provide valuable information for conversion optimization.