A Practical Framework of Conversion Rate Prediction for Online Display Advertising

Abstract

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 challenging 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 challenges 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.