research-article

Exploring the optimal granularity for market segmentation in RTB advertising via computational experiment approach

Authors:

Fei-Yue WangAuthors Info & Claims

Electronic Commerce Research and Applications, Volume 24, Issue C

Pages 68 - 83

https://doi.org/10.1016/j.elerap.2017.07.001

Published: 01 July 2017 Publication History

Abstract

Real Time Bidding (RTB) is a novel business model of online computational advertising, developing rapidly with the integration of Internet economy and big data analysis. It evolves the business logic of online ad-delivery from buying ad-impressions in websites or ad slots to directly buying the best-matched target audiences, and thus can help advertisers achieve the precision marketing. As a critical part of RTB advertising markets, Demand Side Platforms (DSPs) play a central role in matching advertisers with their target audiences via cookie-based data analysis and market segmentation, and their segmentation strategies (especially the choice of granularity) have key influences in improving the effectiveness and efficiency of RTB advertising markets. Based on a mathematical programming approach, this paper studied DSPs strategies for market segmentation, and established a selection model of the granularity for segmenting RTB advertising markets. With the computational experiment approach, we designed three experimental scenarios to validate our proposed model, and the experimental results show that: 1) market segmentation has the potential of improving the total revenue of all the advertisers; 2) with the increasing refinement of the market segmentation granularity, the total revenue has a tendency of a rise first and followed by a decline; 3) the optimal granularity of market segmentation will be significantly influenced by the number of advertisers on the DSP, but less influenced by the number of ad requests. Our findings show the crucial role of market segmentation on the RTB advertising effect, and indicate that the DSPs should adjust their market segmentation strategies according to their total number of advertisers. Our findings also highlight the importance of advertisers as well as the characteristics of the target audiences to DSPs market segmentation decisions.

References

[1]

D. Bergemann, A. Bonatti, Targeting in advertising markets: implications for offline versus online media, The RAND J. Econ., 42 (2011) 417-443.

[2]

M.J.A. Berry, G. Linoff, Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management, Wiley, 2014.

Digital Library

[3]

J. Bian, A. Dong, X. He, S. Reddy, Y. Chang, User action interpretation for online content optimization, IEEE Trans. Knowl. Data Eng., 25 (2013) 2161-2174.

Digital Library

[4]

R.C. Blattberg, S.K. Sen, Market segmentation using models of multidimensional purchasing behavior, J. Market., 38 (1974) 17-28.

[5]

R. Cavallo, P. McAfee, S. Vassilvitskii, Display advertising auctions with arbitrage, Trans. Econ. Comput., 3 (2014) 1-23.

Digital Library

[6]

J. Chen, J. Stallaert, An economic analysis of online advertising using behavioral targeting, MIS Q., 38 (2014) 429-449.

Digital Library

[7]

Y. Chen, P. Berkhin, B. Anderson, Real-time bidding algorithms for performance-based display ad allocation, in: Proceedings of the 17th ACM Conference on Knowledge Discovery and Data Mining. San Diego, California, August 2124, ACM, 2011, pp. 1307-1315.

Digital Library

[8]

X. Cheng, M. Yuan, L. Xu, T. Zhang, Y. Jia, C. Cheng, W. Chen, Big data assisted customer analysis and advertising architecture for real estate, in: 2016 16th International Symposium on Communications and Information Technologies (ISCIT), IEEE, 2016, pp. 312-317.

[9]

H.J. Clayclamp, F.M. William, A theory of market segmentation, J. Market. Res., 5 (1968) 388-394.

[10]

S. Dolnicar, Review of data-driven market segmentation in tourism, J. Travel Tourism Market., 12 (2002) 1-22.

[11]

S. Dolnicar, G. Bettina, Challenging factor cluster segmentation, J. Travel Res., 47 (2008) 63-71.

[12]

S. Dolnicar, S. Kaiser, K. Lazarevski, F. Leisch, Biclustering overcoming data dimensionality problems in market segmentation, J. Travel Res., 51 (2012) 41-49.

[13]

S. Faggian, L. Grosset, Optimal advertising strategies with age-structured goodwill, Math. Methods Oper. Res., 78 (2013) 259-284.

[14]

J. Feldman, V.S. Mirrokni, S. Muthukrishnan, Auctions with intermediaries, in: Proceedings of the 11th ACM Conference on Electronic Commerce. Massachusetts, USA, June 711, ACM, 2010, pp. 23-32.

Digital Library

[15]

A. Ghosh, B.I.P. Rubinstein, S. Vassilvitskii, Adaptive bidding for display advertising, in: Proceedings of the 18th International Conference on World Wide Web. Madrid, Spain, April 2024, ACM, 2009, pp. 251-260.

Digital Library

[16]

X. Gong, X. Guo, R. Zhang, X. He, A. Zhou, Search behavior based latent semantic user segmentation for advertising targeting, in: 13rd IEEE International Conference on Data Mining (ICDM2013). Dallas, Texas, December 710, IEEE, 2013, pp. 211-220.

[17]

L. Grosset, B. Viscolani, Advertising for the introduction of an age-sensitive product, Optimal Control App. Methods, 26 (2005) 157-167.

[18]

A.M. Hughes, Strategic Database Marketing, Probus Publishing Company, Chicago, 1994.

[19]

W.A. Kamakura, G. Russell, A probabilistic choice model for market segmentation and elasticity structure, J. Market. Res., 26 (1989) 379-390.

[20]

K.Y. Kim, B.G. Lee, Marketing insights for mobile advertising and consumer segmentation in the cloud era: a Q-R hybrid methodology and practices, Technol. Forecasting Social Change, 91 (2015) 78-92.

[21]

P. Kotler, Marketing Management: Analysis, Planning, Implementation, and Control, Englewood Cliffs, NJ, Prentice-Hall, 1994.

[22]

P. Kotler, V. Wong, J. Saunders, G. Armstrong, Principles of Marketing, Pearson Education, Essex, 2005.

[23]

O. Kucukemiroglu, Market segmentation by using consumer lifestyle dimensions and ethnocentrism: an empirical study, Eur. J. Market., 33 (1999) 470-487.

[24]

F.E. Kydland, E.C. Prescott, The computational experiment: an econometric tool, J. Econ. Perspect., 10 (1996) 69-85.

[25]

J. Levin, P. Milgrom, Online advertising: heterogeneity and conflation in market design, Am. Econ. Rev., 100 (2010) 603-607.

[26]

Li, X., Guan, D., 2014. Programmatic buying bidding strategies with win rate and winning price estimation in real time mobile advertising. In: Proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Tainan, Taiwan, May 1316, 447460.

[27]

S.K. Lo, A.Y. Hsieh, Y.P. Chiu, Keyword advertising is not what you think: clicking and eye movement behaviors on keyword advertising, Electron. Commerce Res. App., 13 (2014) 221-228.

Digital Library

[28]

Mansour, Y., Muthukrishnan, S., Nisan, N., 2012. Doubleclick ad exchange auction. arXiv preprint arXiv:1204.0535.

[29]

J. Mazanee, Market Segmentation, in: Encyclopedia Tourism, Routledge, London, 2000.

[30]

Mobius, M., Nazerzadeh, H., Lewis, G., et al., 2012. Buy-it-now or take-a-chance: a new pricing mechanism for online advertising. 2012 Meeting Papers, No. 443, Society for Economic Dynamics.

[31]

S. Muthukrishnan, Ad exchanges: research issues, Springer, Berlin Heidelberg, 2009.

[32]

J.H. Myers, E.M. Tauber, Market Structure Analysis, Marketing Classics Press, 2011.

[33]

R. Myerson, Optimal auction design, Math. Oper. Res., 6 (1981) 58-73.

Digital Library

[34]

Patki, N., Wedge, R., Veeramachaneni, K., 2016. The synthetic data vault. In: Proceedings of the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA2016). Montreal, Canada, October 1719, pp. 399410.

[35]

V. Prakash, Segmentation of womens market based on personal values and the means-end chain model: a framework for advertising strategy, NA-Adv. Consum. Res., 13 (1986) 215-220.

[36]

Saia, R., Boratto, L., Carta, S., 2015. A latent semantic pattern recognition strategy for an untrivial targeted advertising. In: 4th International Congress on Big Data. New York, USA, June 27-July 2, 491498.

Digital Library

[37]

R. Saia, L. Boratto, S. Carta, Binary sieves: toward a semantic approach to user segmentation for behavioral targeting, Future Gener. Comput. Syst., 64 (2016) 186-197.

Digital Library

[38]

W.R. Smith, Product differentiation and market segmentation as alternative marketing strategies, J. Market., 21 (1956) 3-8.

[39]

Tu, S., Lu, C., 2010. Topic-based user segmentation for online advertising with latent dirichlet allocation. In: Proceedings of the 6th International Conference on Advanced Data Mining and Applications. Chongqing, China, November 1921, pp. 259269.

Digital Library

[40]

F.Y. Wang, Artificial societies, computational experiments, and parallel systems: a discussion on computational theory of complex social-economic systems, Complex Syst. Complexity Sci., 1 (2004) 25-35.

[41]

P.A. Weaver, K.W. McCleary, A market segmentation study to determine the appropriate ad/model format for travel advertising, J. Travel Res., 23 (1984) 12-16.

[42]

M. Wedel, W. Kamakura, Market Segmentation: Conceptual and Methodological Foundations, Springer, 1999.

[43]

M. Wedel, W.A. Kamakura, Market Segmentation: Conceptual and Methodological Foundations, Springer Science & Business Media, 2012.

[44]

J.T. Wei, S.Y. Lin, H.H. Wu, A review of the application of RFM model, Afr. J. Bus. Manage., 4 (2010) 4199.

[45]

A. Weinstein, Handbook of Market Segmentation: Strategic Targeting for Business and Technology Firms, Routledge, 2013.

[46]

R.S. Wu, P.H. Chou, Customer segmentation of multiple category data in e-commerce using a soft-clustering approach, Electron. Commerce Res. App., 10 (2011) 331-341.

Digital Library

[47]

X. Wu, J. Yan, N. Liu, S. Yan, Y. Chen, Z. Chen, Probabilistic latent semantic user segmentation for behavioral targeted advertising, in: Proceedings of the 3rd International Workshop on Data Mining and Audience Intelligence for Advertising. Paris, France, June 28, ACM, 2009, pp. 10-17.

Digital Library

[48]

J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, Z. Chen, How much can behavioral targeting help online advertising?, in: Proceedings of the 18th International Conference on World Wide Web. Madrid, Spain, April 2024, ACM, 2009, pp. 261-270.

Digital Library

[49]

Y. Yuan, D. Zeng, Co-evolution-based mechanism design for sponsored search advertising, Electron. Commerce Res. App., 11 (2012) 537-547.

Digital Library

[50]

Y. Yuan, F.-Y. Wang, J. Li, R. Qin, A survey on real time bidding advertising, in: Proceedings of the 2014 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI2014). Qingdao, China, October 810, IEEE, 2014, pp. 418-423.

[51]

Zhang, C., Zhang, E., 2014. Optimized bidding algorithm of real time bidding in online ads auction. In: Proceedings of the 21th International Conference on Management Science & Engineering. Helsinki, Finland, August 1719, pp. 3342.

[52]

Zhang, W., Yuan, S., Wang, J., et al., 2014. Real-time bidding benchmarking with ipinyou dataset. arXiv preprint arXiv:1407.7073.

[53]

T. Zhang, X. Cheng, M. Yuan, L. Xu, C. Cheng, K. Chao, Mining target users for mobile advertising based on telecom big data, in: 16th International Symposium on Communications and Information Technologies (ISCIT), IEEE, 2016, pp. 296-301.

[54]

Zins, A.H., 2008. Market segmentation in tourism: A critical review of 20 years research effort. Change Management in Tourism, From Old to New Tourism. 289301.

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cover image Electronic Commerce Research and Applications

Electronic Commerce Research and Applications Volume 24, Issue C

July 2017

89 pages

ISSN:1567-4223

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Copyright © Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2017

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Cited By

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Li JYuan YWang SWang F(2018)Transaction Queuing Game in Bitcoin BlockChain2018 IEEE Intelligent Vehicles Symposium (IV)10.1109/IVS.2018.8500403(114-119)Online publication date: 26-Jun-2018
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Qin RNi XYuan YLi JWang F(2017)Revenue models for demand side platforms in real time bidding advertising2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2017.8122644(438-443)Online publication date: 5-Oct-2017
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