The real-time bidding (RTB), aka programmatic buying, has recently become the
fastest growing area in online advertising. Instead of bulking buying and
inventory-centric buying, RTB mimics stock exchanges and utilises computer
algorithms to automatically buy and sell ads in real-time; It uses per
impression context and targets the ads to specific people based on data about
them, and hence dramatically increases the effectiveness of display
advertising. In this paper, we provide an empirical analysis and measurement of
a production ad exchange. Using the data sampled from both demand and supply
side, we aim to provide first-hand insights into the emerging new impression
selling infrastructure and its bidding behaviours, and help identifying
research and design issues in such systems. From our study, we observed that
periodic patterns occur in various statistics including impressions, clicks,
bids, and conversion rates (both post-view and post-click), which suggest
time-dependent models would be appropriate for capturing the repeated patterns
in RTB. We also found that despite the claimed second price auction, the first
price payment in fact is accounted for 55.4% of total cost due to the
arrangement of the soft floor price. As such, we argue that the setting of soft
floor price in the current RTB systems puts advertisers in a less favourable
position. Furthermore, our analysis on the conversation rates shows that the
current bidding strategy is far less optimal, indicating the significant needs
for optimisation algorithms incorporating the facts such as the temporal
behaviours, the frequency and recency of the ad displays, which have not been
well considered in the past.

本文提供对 RTB 新兴的印象销售基础架构的实证分析和测量，展示了其出价行为，并指出时间依赖模型、优化算法等问题未得到充分考虑。