Optimizing the Segmentation Granularity for RTB Advertising Markets with a Two-stage Resale Model
Qin, Rui; Yuan, Yong; Li, Juanjuan; Wang, Fei-Yue
2016-10-09
Conference Name2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC2016)
Source PublicationProceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC2016)
Conference DateOct. 9-12, 2016
Conference PlaceBudapest, Hungary
AbstractReal Time Bidding (RTB) is an emerging business model and a popular research topic of online advertising markets. Using cookie-based big-data analysis, RTB advertising platforms have the ability to precisely identify the features and preferences of online users, segment them into various kinds of niche markets, and thus achieve the precision marketing via delivering advertisements to the best-matched users. The segmentation granularity used by such platforms, typically referred to as the Demand Side Platforms (DSPs), plays a central role in the effectiveness and efficiency of the RTB ecosystem. In practice, fine-grained user segmentations may lead to increased value-per-clicks and bid prices from advertisers, but at the same time reduced competition and possibly decreased bid prices in each niche market. This motivates our research on the optimal segmentation granularity to solve this dilemma faced by DSPs. Using a RTB market model with two-stage resales, we analyzed DSPs’ segmentation strategies taking the revenues of both advertisers and DSPs into consideration. We also validated our proposed model and analysis using the computational experiment approach, and the experimental results indicate that with the increasing of segmentation granularity, the weighted sum of the DSP and advertisers’ revenues tends to first rise and then decline in all weight-value cases, and the optimal granularity is greatly influenced by the value of weights. Our work highlights the need for DSPs of moderately using, instead of overusing, the online big data for maximized revenues.
KeywordReal Time Bidding Demand Side Platforms Market Segmentation Two-stage Resale Model Computational Experiment
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19724
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 AuthorYuan, Yong
AffiliationThe State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Qin, Rui,Yuan, Yong,Li, Juanjuan,et al. Optimizing the Segmentation Granularity for RTB Advertising Markets with a Two-stage Resale Model[C],2016.
Files in This Item: Download All
File Name/Size DocType Version Access License
Optimizing the Segme(1456KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Qin, Rui]'s Articles
[Yuan, Yong]'s Articles
[Li, Juanjuan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Qin, Rui]'s Articles
[Yuan, Yong]'s Articles
[Li, Juanjuan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Qin, Rui]'s Articles
[Yuan, Yong]'s Articles
[Li, Juanjuan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Optimizing the Segmentation Granularity for RTB Advertising Markets with a Two-stage Resale Model.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.