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Statistical Management of Pay-Per-Click Processes for Search Engines

  • David A. WooffEmail author
  • Jillian M. Anderson
  • Amin Jamalzadeh
Chapter

Abstract

Suppose you want to buy a dishwasher. What you might do is go to a search engine such as Google or Bing and type “dishwasher” in the search field. If you are using Google, what you may see are some sponsored links (adverts), as well as the results of natural search offered by Google’s search algorithm. The sponsored links appear because the keyword you typed is a keyword that a company has paid Google to display whenever someone searches for it. Broadly what happens is that if you then click on the advert, the sponsoring company will pay Google—or the search engine you used—a small amount. This is called Pay-Per-Click (PPC). We describe statistical models and methods which are used to automate and optimize daily PPC bid-price setting over portfolios which can contain hundreds of thousands of products and keywords, with the aim of maximizing the flow of customers and revenue to online retailers.

Keywords

Online Retailer Forecast Revenue Generic Search Engine Multiple Choice Knapsack Problem Natural Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Part of this research was funded by Knowledge Transfer Partnership KTP–7499, funded by Summit and by the UK Technology Strategy Board. We are grateful to Summit for providing data and to colleagues there for providing expertise.

References

  1. 1.
    Summit.: Back to the future for performance marketing. White paper on predictive analytics (2015). http://www.summit.co.uk/predictive-analytics-for-performance-marketing/
  2. 2.
    Wooff, D.A., Anderson, J.M.: Time-weighted multi-touch attribution and channel relevance in the customer journey to online purchase. J. Stat. Theory Pract. 9(2), 227–249 (2015). doi: 10.1080/15598608.2013.862753 MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wooff, D.A., Jamalzadeh, A.: Robust and scale-free effect sizes for non-Normal two-sample comparisons, with applications in e-commerce. J. Appl. Stat. 40(11), 2495–2515 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • David A. Wooff
    • 1
    Email author
  • Jillian M. Anderson
    • 2
  • Amin Jamalzadeh
    • 2
  1. 1.Department of Mathematical SciencesDurham UniversityDurhamUK
  2. 2.Summit, Albion MillsKingston-upon-HullUK

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