Optimized Cost per Click in Taobao Display Advertising
This addresses the need for improved advertising efficiency and revenue optimization for large-scale e-commerce platforms like Taobao, representing an incremental improvement over traditional fixed-bid methods.
The paper tackled the problem of inefficient traffic allocation in Taobao's display advertising system due to fixed bids, proposing an optimized cost per click (OCPC) strategy that automatically adjusts bids at the page view level, resulting in substantially better performance in online A/B tests.
Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to display in tens of milliseconds. Common pricing methods include cost per mille (CPM) and cost per click (CPC). Traditional advertising systems target certain traits of users and ad placements with fixed bids, essentially regarded as coarse-grained matching of bid and traffic quality. However, the fixed bids set by the advertisers competing for different quality requests cannot fully optimize the advertisers' key requirements. Moreover, the platform has to be responsible for the business revenue and user experience. Thus, we proposed a bid optimizing strategy called optimized cost per click (OCPC) which automatically adjusts the bid to achieve finer matching of bid and traffic quality of page view (PV) request granularity. Our approach optimizes advertisers' demands, platform business revenue and user experience and as a whole improves traffic allocation efficiency. We have validated our approach in Taobao display advertising system in production. The online A/B test shows our algorithm yields substantially better results than previous fixed bid manner.