Real-time bidding campaigns optimization using user profile settings

Abstract

Real-Time bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users’ profiles and the publishers’ websites. Most studies about optimising RTB campaigns are focused on the bidding strategy; estimating the best value for each bid. However, our research is focused on optimising RTB campaigns by finding out configurations that maximise both the number of impressions and the average profitability of the visits. An online campaign configuration generally consists of a set of parameters along with their values such as {Browser = “Chrome”, Country = “Germany”, Age = “20–40” and Gender = “Woman”}. The experiments show that, when the number of required visits by advertisers is low, it is easy to find configurations with high average profitability, but as the required number of visits increases, the average profitability diminishes. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns’ profitability. In particular, the presented study considers the following complementary strategies to increase profitability: (1) selecting multiple configurations with a small number of visits rather than a unique configuration with a large number of visits, (2) discarding visits according to certain cost and profitability thresholds, (3) analysing a reduced space of the dataset and extrapolating the solution over the whole dataset, and (4) increasing the search space by including solutions below the required number of visits. The developed campaign optimisation methodology could be offered by RTB and other advertising platforms to advertisers to make their campaigns more profitable.