How much can behavioral targeting help online advertising?

  • Authors:

  • Jun Yan;Ning Liu;Gang Wang;Wen Zhang;Yun Jiang;Zheng Chen

  • Affiliations:

  • Microsoft Research Asia, beijing, China;Microsoft Research Asia, beijing, China;Microsoft Research Asia, beijing, China;University of Science & Technology , HeFei, China;ShangHai Jiao Tong University , ShangHai, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 18th international conference on World wide web
  • Year:
  • 2009

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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.