Abstract
Programmatic buying popularly known as real-time bidding (RTB) is a key ascendancy in online advertising. The Ad Tech industry is experiencing sustained growth, especially due to the increased use of mobile devices. While data has become essential for targeting and ad performance, data businesses have become difficult to differentiate due to their proliferation, as well as limitations of attribution. This provides an opportunity for Big Data practitioners to leverage this data and use machine learning to improve efficiency and make more profits. Taking such an opportunity we came up with an application of a machine learning algorithm, distributed back propagation neural network (d-bpnn) to predict bid prices in a real-time bidding system. This paper depicts how d-bpnn is used to achieve less eCPM (effective Cost Per Mille) for advertisers while preserving win rate and budget utilization.
