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.
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Chen, Y., Berkhin, P., Anderson, B., et al.: Real-time bidding algorithms for performance-based display ad allocation. In: 17th ACM Conference on Knowledge Discovery and Data Mining, pp. 1307–1315. ACM Press, SanDiego (2011)
Ghosh, A., Rubinstein, B.I.P., Vassilvitskii, S., et al.: Adaptive bidding for display advertising. In: 18th International Conference on World Wide Web. ACM Press, Madrid (2009)
Li, X., Guan, D.: Programmatic buying bidding strategies with win rate and winning price estimation in real time mobile advertising. In: 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 447–460 (2014)
Wang, F.Y.: Artificial societies, computational experiments, and parallel systems: a discussion on computational theory of complex social-economic systems. Complex Syst. Complex. Sci. 1(4), 25–35 (2004)
Zhang, W., Yuan, W., Wang, W., Shen, X.: Real-time bidding benchmarking with iPinYou dataset. UCL Technical report (2014)
Zhang, C., Zhang, E.: Optimized bidding algorithm of real time bidding in online ads auction. In: 21th International Conference on Management Science and Engineering, pp. 33–42 (2014)
Acknowledgements
This work is partially supported by NSFC (#71472174, #71102117, #71232006, #61533019, #61233001) and the Early Career Development Award of SKLMCCS (Y3S9021F36, Y3S9021F2K).
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© 2015 Springer Science+Business Media Singapore
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Qin, R., Yuan, Y., Wang, F., Li, J. (2015). Analyzing the Segmentation Granularity of RTB Advertising Markets: A Computational Experiment Approach. In: Zhang, X., Sun, M., Wang, Z., Huang, X. (eds) Social Media Processing. SMP 2015. Communications in Computer and Information Science, vol 568. Springer, Singapore. https://doi.org/10.1007/978-981-10-0080-5_21
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DOI: https://doi.org/10.1007/978-981-10-0080-5_21
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