Abstract
Online advertising is a high and a constantly growing business with an elaborate complexity. An important position is occupied by intermediaries, such as ad networks, marketing agencies and companies specialized in delivering software for managing and displaying advertising campaigns. Efficient use of advertising budgets and maximizing profits from ad impressions are the common goal of all players. This goal is usually achieved by manual management of advertising campaigns. Such management requires a very good prediction of Web users’ behavior and web site traffic. We have tested the performance of an ad emission simulator for various campaigns and different types of constraints (e.g. capping or targeting). Real data from completed campaigns has been used for the experiment. It has been shown that predicting performance of advertising campaigns that have constraints is considerably more difficult than predicting simple campaigns. Our result applies even if we use a Web traffic sample from the current period (idealized Web traffic prediction) and a simulator that practically emulates ad server operation (the most realistic prediction approach).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Internet Advertising Board. IAB Internet Advertising Revenue Report, 2011 First Six Months Results (2011), http://www.iab.net/media/file/IAB-HY-2011-Report-Final.pdf
OVK. OVK Online Report 2011/02 Zahlen und Trend im Ueberblick (2011), http://www.bvdw.org/fileadmin/bvdw-shop/ovk-report-2011-2.pdf
Feldman, J., Mehta, A., Mirrokni, V., Muthukrishnan, S.: Offline Optimization for Online Ad Allocation. In: Fifth Workshop on Ad Auctions, Stanford (2009)
Alaei, S., Arcaute, E., Khuller, S., Ma, W., Malekian, A., Tomlin, J.: Online Allocation of Display Advertisements Subject to Advanced Sales Contracts. In: s.l.: Proc. ADKDD 2009 (2009)
Jiang, J., Papavassiliou, S.: Enhancing network traffic prediction and anomaly detection via statistical network traffic separation and combination strategies. Computer Communications 29, 1627–1638 (2006)
Sang, A., Li, S.Q.: A predictability analysis of network traffic. Computer Networks 39, 329–345 (2002)
Hillard, D., Manavoglu, E., Raghavan, H., Leggetter, C., Cantú-Paz, E., Iyer, R.: The sum of its parts: reducing sparsity in click estimation with query segments. Information Retrieval 14, 315–336 (2011)
Huang, C.Y., Lin, C.S.: Modeling the audience’s banner ad exposure for internet advertising planning. Journal of Advertising 35(2), 123–136 (2006)
Jaworska, J., Sydow, M.: Behavioural Targeting in On-Line Advertising: An Empirical Study. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 62–76. Springer, Heidelberg (2008)
Martin, S., Anders, C.: Individualized, Real-Time, Interactive E-commerce Auction, United States Patent No: 6,606,607 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nielek, R., Parzych, D., Sepczuk, D., Wierzbicki, A., Wysoczanski, J. (2013). Forecasting Online Advertising Campaigns in the Wild. In: Järveläinen, J., Li, H., Tuikka, AM., Kuusela, T. (eds) Co-created Effective, Agile, and Trusted eServices. ICEC 2013. Lecture Notes in Business Information Processing, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39808-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-39808-7_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39807-0
Online ISBN: 978-3-642-39808-7
eBook Packages: Computer ScienceComputer Science (R0)