Adaptive Designs for Optimizing Online Advertisement Campaigns
We investigate the problem of adaptive targeting for real-time bidding in online advertisement using independent advertisement exchanges. This is a problem of making decisions based on information extracted from large data sets related to previous experience. We describe an adaptive strategy for optimizing the click through rate which is a key criterion used by advertising platforms to measure the efficiency of an advertisement campaign. We also provide some results of statistical analysis of real data.
KeywordsUser Agent Advertising Campaign Online Advertisement Sequential Splitting Click Through Rate
The paper is a result of collaboration of Crimtan, a provider of proprietary ad technology platforms and the 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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