A Practical Framework of Conversion Rate Prediction for Online Display Advertising
Quan Lu, Shengjun Pan, Liang Wang, Junwei Pan, Fengdan Wan, and Hongxia Yang
Cost-per-action (CPA), or cost-per-acquisition, has become the primary campaign performance objective in online advertising industry. As a result, accurate conversion rate (CVR) prediction is crucial for any real-time bidding (RTB) platform. However, CVR prediction is quite chal- lenging due to several factors, including extremely sparse conversions, delayed feedback, attribution gaps between the platform and the third party, etc. In order to tackle these challenges, we proposed a practical framework that has been successfully deployed on Yahoo! BrightRoll, one of the largest RTB ad buying platforms. In this paper, we first show that over-prediction and the resulted over-bidding are fundamental chal- lenges for CPA campaigns in a real RTB environment. We then propose a safe prediction framework with conversion attribution adjustment to handle over-predictions and to further alleviate over-bidding at different levels. At last, we illustrate both offline and online experimental results to demonstrate the effectiveness of the framework.