Analyzing the Segmentation Granularity of RTB Advertising Markets: A Computational Experiment Approach
Qin, Rui; Yuan, Yong; Wang, Fei-Yue; Li, Juanjuan; Rui Qin
2015-11
Conference NameNational Conference of Social Media Processing (SMP2015)
Source PublicationSocial Media Processing
Conference DateNov. 16-17, 2015
Conference PlaceGuangzhou, China
AbstractReal Time Bidding (RTB) is an emerging business model of online computational advertising with the rise of Internet and big data. It can help advertisers achieve the precision marketing through evolving the traditional business logic from buying ad-impressions to directly buying the matched target audiences. As an important part of RTB markets, Demand Side Platforms (DSPs) play a critical role in matching advertisers with their target audiences via market segmentation, and their segmentation strategies (especially the choice of granularity) have key influences in improving the efficiency of RTB markets. This paper studied DSPs' strategies for market segmentation, and established a selection model of the granularity for segmenting RTB markets. We proposed to validate our model using a computational experiment approach, and the experimental results show that the market segmentation granularity has the potential of improving both the total revenue of all the advertisers and the expected revenue for each advertiser.
KeywordReal Time Bidding Demand Side Platforms Market Segmentation Granularity Computational Experiments Precision Marketing
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11567
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding AuthorRui Qin
Recommended Citation
GB/T 7714
Qin, Rui,Yuan, Yong,Wang, Fei-Yue,et al. Analyzing the Segmentation Granularity of RTB Advertising Markets: A Computational Experiment Approach[C],2015.
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