Real-time bidding for online advertising: measurement and analysis
By Shuai Yuan, Jun Wang, Xiaoxue Zhao. International Workshop on Data Mining for Online Advertising, 2013
312 citations recorded by the source corpus.
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.
Subfields: Computer Networks and Communications, Management Science and Operations Research, Marketing
View Paper: https://www.semanticscholar.org/paper/ba121dde8674897d263d2442b37c45d8be3bbb12
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Citations in the corpus
- Optimized Cost per Click in Taobao Display Advertising (2017, background): “And lots of research [25, 24, 23, 26] has found effective and efficient bidding strategies to maximize unilateral economic surplus of a party, such as advertisers, consumers and intermediary platforms.”
- Optimal real-time bidding for display advertising (2014, methodology): “First, the bids are not determined by pre-defined keywords [38], but are based on impression level features.”
- Real-Time Bidding by Reinforcement Learning in Display Advertising (2017, background): “, bidding for every single ad impression in real time when it is just being generated by a user visit [18, 30] .”