Jun Yan
ABSTRACT
Behavioral Targeting (BT) is a technique used by online advertisers to increase the effectiveness of their campaigns, and is playing an increasingly important role in the online advertising market. However, it is underexplored in academia when looking at how much BT can truly help online advertising in commercial search engines. To answer this question, in this paper we provide an empirical study on the click-through log of advertisements collected from a commercial search engine. From the comprehensively experiment results on the sponsored search log of the commercial search engine over a period of seven days, we can draw three important conclusions: (1) Users who clicked the same ad will truly have similar behaviors on the Web; (2) Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search; (3) Using the short term user behaviors to represent users is more effective than using the long term user behaviors for BT. The statistical t-test verifies that all conclusions drawn in the paper are statistically significant. To the best of our knowledge, this work is the first empirical study for BT on the click-through log of real world ads.
- A. Broder, M. Fontoura, V. Josifovski and L. Riedel. A semantic approach to contextual advertising. In Proceedings of SIGIR '07 (Amsterdam, July 2007), ACM Press, 559--566. Google Scholar
Digital Library
- A. S. Das, M. Datar, A. Garg and S. Rajaram. Google news personalization: scalable online collaborative filtering. In Proceedings of WWW '07 (Banff, May 2007), ACM Press, 271--280. Google ScholarDigital Library
- C. J. van Rijsbergen, S. E. Robertson and M. F. Porter. New models in probabilistic information retrieval. British Library Research and Development Report, No. 5587, 1980.Google Scholar
- D. C. Fain and J. O. Pedersen. Sponsored search: a brief history. In Bulletin of the American Society for Information Science and Technology, 2005.Google Scholar
- D.R. Cox and D.V. Hinkley. Theoretical statistics. Chapman and Hall, London, 1974.Google ScholarCross Ref
- G. Hripcsak and A.S. Rothschild. Agreement, the F--Measure, and reliability. Information Retrieval Journal of the American Medical Informatics Association, 2 (May 2005), 296--298.Google ScholarCross Ref
- G. Karypis. CLUTO: a software package for clustering high-Dimensional data sets. University of Minnesota, Dept. of Computer Science.Google Scholar
- G.Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. Information Processing and Management: an International Journal, 24 (1988), 513--523. Google ScholarDigital Library
- P. Indyk and R. Motwani. Approximate nearest neighbor: towards removing the curse of dimensionality. In Proceedings of the 30th Annual ACM Symposium on Theory of Computing (Dallas, May 1998), ACM Press, 604--613. Google ScholarDigital Library
- T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman and A. Wu. An efficient K-means clustering algorithm: Analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell., 24 (July 2000), 881--892. Google ScholarDigital Library
- Adlink https://www.google.com/adsense/login/en_US/?gsessionid=Dc28hZShnCIGoogle Scholar
- Specificmeida http://www.specificmedia.co.uk/Google Scholar
- Almond Net http://www.almondnet.com/Google Scholar
- Blue Lithium http://www.bluelithium.com/Google Scholar
- http://en.wikipedia.org/wiki/Behavioral_targetingGoogle Scholar
- Burst http://www.burstmedia.com/Google Scholar
- Double Click http://www.doubleclick.com/products/dfa/index.aspxGoogle Scholar
- NebuAd http://www.nebuad.com/Google Scholar
- Phorm http://www.phorm.com/Google Scholar
- Revenue Science http://www.revenuescience.com/advertisers/advertiser_solutions.aspGoogle Scholar
- TACODA http://www.tacoda.com/Google Scholar
- Yahoo! Smart Ads http://advertising.yahoo.com/marketing/smartads/Google Scholar
Index Terms
- How much can behavioral targeting help online advertising?
Recommendations
Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of ...
An economic analysis of online advertising using behavioral targeting
Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and ...
First workshop on targeting and ranking for online advertising
WWW '08: Proceedings of the 17th international conference on World Wide WebOnline advertising is a rapidly growing, multi-billion dollar industry. It has become a significant element of the Web browsing experience. Online advertising providers use sophisticated ad targeting and ranking algorithms with the dual aim of ...
Comments