Paper Info
Title
Analyzing the Segmentation Granularity of RTB Advertising Markets: A Computational Experiment Approach.
Abstract
Real 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.
Year
DOI
Venue
2015
10.1007/978-981-10-0080-5_21
Communications in Computer and Information Science
Keywords
DocType
Volume
Real time bidding,Demand side platforms,Market segmentation granularity,Computational experiments,Precision marketing
Conference
568
ISSN
Citations 
PageRank 
1865-0929
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Rui Qin16510.85
Yong Yuan223931.09
Fei-Yue Wang35273480.21
Juanjuan Li47114.17