Adaptive Targeting in Online Advertisement: Models Based on Relative Influence of Factors
We consider the problem of adaptive targeting for real-time bidding for internet advertisement. This problem involves making fast decisions on whether to show a given ad to a particular user. For demand partners, these decisions are based on information extracted from big data sets containing records of previous impressions, clicks and subsequent purchases. We discuss several criteria which allow us to assess the significance of different factors on probabilities of clicks and conversions. We then devise simple strategies that are based on the use of the most influential factors and compare their performance with strategies that are much more computationally demanding. To make the numerical comparison, we use real data collected by Crimtan in the process of running several recent ad campaigns.
KeywordsOnline advertisement Real-time bidding Adaptive targeting Big data Conversion rate
The paper is a result of collaboration of Crimtan, a provider of proprietary ad technology platform and University of Cardiff. Research of the third author was supported by the Russian Science Foundation, project No. 15-11-30022 “Global optimization, supercomputing computations, and application”.
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