Mining the Long Tail of Search Queries - Finding Profitable Patterns

Michael Meisel, Maik Benndorf, Andreas Ittner

2013

Abstract

Many search engine marketing campaigns contain a lot of different search queries with a low frequency referred as "Long Tail". It is not possible to draw reliable conclusions about the performance of a specific search query with low frequency regarding a business goal because of its limited sample size. In this paper we present a method for finding profitable patterns in the long tail of search queries. The method aggregates search queries based on mined patterns and rejects the non profitable groups. We applied our method to a search engine marketing campaign with over 10,000 different search queries and performed an offline test and an online A/B-test to measure the performance of the method.

References

  1. A. Ghose, S. Y. (2009). An empirical analysis of search engine advertising: Sponsored search in electronic markets. In Management Science Volume 55 Issue 10, Pages 1605-1622. INFORMS.
  2. Anderson, C. (2004). The long tail. In Wired Magazine 12(10), Pages 170-177. Wired Magazine.
  3. Anderson, C. (2006). The Long Tail: Why the Future of Business is Selling Less of More. Hyperion, New York.
  4. B. Skiera, J. Eckert, O. H. (2010). An analysis of the importance of the long tail in search engine marketing. In Journal Electronic Commerce Research and Applications Volume 9 Issue 6, Pages 488-494. Elsevier Science Publishers.
  5. E. Brynjolfsson, Y. Hu, D. S. (2011). Goodbye ff pareto principle, hello long tail: The effect of search costs on the concentration of product sales. In Journal Management Science Volume 57 Issue 8, Pages 1373-1386. INFORMS.
  6. E. Brynjolfsson, Y. Hu, M. D. S. (2007). From niches to riches: Anatomy of the long tail. In MIT Sloan Management Review Volume 47 Issue 4, Pages 67-71. MIT.
  7. G. Xu, S. Yang, H. L. (2009). Named entity mining from click-through data using weakly supervised latent dirichlet allocation. In KDD 7809 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, Pages 1365- 1374. ACM.
  8. J. Feng, H. K. Bhargava, D. M. P. (2007). Implementing sponsored search in web search engines: Computational evaluation of alternative mechanisms. In INFORMS Journal on Computing Volume 19 Issue 1, Pages 137-148. INFORMS.
  9. M. Pasca, B. v. D. (2007). What you seek is what you get: extraction of class attributes from query logs. In IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence, Pages 2832-2837. Morgan Kaufmann Publishers Inc.
  10. P. Rusmevichientong, D. P. W. (2006). An adaptive algorithm for selecting profitable keywords for searchbased advertising services. In Proceeding EC 7806 Proceedings of the 7th ACM conference on Electronic commerce, Pages 260-269. ACM.
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Paper Citation


in Harvard Style

Meisel M., Benndorf M. and Ittner A. (2013). Mining the Long Tail of Search Queries - Finding Profitable Patterns . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: SSTM, (IC3K 2013) ISBN 978-989-8565-75-4, pages 225-229. DOI: 10.5220/0004521602250229


in Bibtex Style

@conference{sstm13,
author={Michael Meisel and Maik Benndorf and Andreas Ittner},
title={Mining the Long Tail of Search Queries - Finding Profitable Patterns},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: SSTM, (IC3K 2013)},
year={2013},
pages={225-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004521602250229},
isbn={978-989-8565-75-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: SSTM, (IC3K 2013)
TI - Mining the Long Tail of Search Queries - Finding Profitable Patterns
SN - 978-989-8565-75-4
AU - Meisel M.
AU - Benndorf M.
AU - Ittner A.
PY - 2013
SP - 225
EP - 229
DO - 10.5220/0004521602250229