Skip to main content

Online Controlled Experiments at Large Scale in Society 5.0

  • Chapter
  • First Online:
Book cover Optimization in Large Scale Problems

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 152))

  • 1124 Accesses

Abstract

For the digital parts of businesses in Society 5.0, such as web site and mobile applications, manual testing is impractical and slow. Instead, implementation of ideas can now be evaluated with scientific rigor using online controlled experiments (A/B tests), which provide trustworthy reliable assessments of the impact of the implementations to key metrics of interest. This chapter shows how online controlled experiments can be run at large scale.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Box, G.E.P., Stuart Hunter, J., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery, 2nd edn. Wiley, Hoboken (2005)

    MATH  Google Scholar 

  2. Crook, T., Frasca, B., Kohavi, R., Longbotham, R.: Seven pitfalls to avoid when running controlled experiments on the web. KDD ‘09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1105–1114. (2009)

    Google Scholar 

  3. Dmitriev, P., Frasca, B., Gupta, S., Kohavi, R., Vaz, G.: Pitfalls of long-term online controlled experiments. IEEE Int. Conf. Big Data. Washington, DC. 1367–1376 (2016). https://doi.org/10.1109/BigData.2016.7840744

  4. Dmitriev, P., Gupta, S., Kim, D.W., Vaz, G.: A dirty dozen: twelve common metric interpretation pitfalls in online controlled experiments. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), pp. 1427–1436. ACM, Halifax (2017). https://doi.org/10.1145/3097983.3098024

    Chapter  Google Scholar 

  5. Fabijan, A., Gupchup, J., Gupta, S., Omhover, J., Qin, W., Vermeer, L., Dmitriev, P.: Diagnosing sample ratio mismatch in online controlled experiments: a taxonomy and rules of thumb for practitioners. In: KDD ‘19: The 25th SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, Anchorage (2019)

    Google Scholar 

  6. Gerber, A.S., Green, D.P.: Field Experiments: Design, Analysis, and Interpretation. W. W. Norton & Company, New York (2012). https://www.amazon.com/Field-Experiments-Design-Analysis-Interpretation/dp/0393979954

    Google Scholar 

  7. Gupta, S., Kohavi, R., Tang, D., Ya, X., et al.: Top challenges from the first practical online controlled experiments summit. 21(1). https://bit.ly/OCESummit1 (2019)

  8. Hern, A.: Why Google has 200m reasons to put engineers over designers. The Guardian. Feb 5. https://www.theguardian.com/technology/2014/feb/05/why-google-engineers-designers (2014)

  9. Hohnhold, H., O’Brien, D., Tang, D.: Focus on the long-term: it’s better for users and business. In: Proceedings 21st Conference on Knowledge Discovery and Data Mining (KDD 2015). ACM, Sydney (2015). http://dl.acm.org/citation.cfm?doid=2783258.2788583

    Google Scholar 

  10. Holson, L.M.: Putting a bolder face on google. NY Times. Feb 28. https://www.nytimes.com/2009/03/01/business/01marissa.html (2009)

  11. Keidanren.: Society 5.0: co-creating the future. Keidanren, Japan Business Federation. http://www.keidanren.or.jp/en/policy/2018/095_booklet.pdf (2018)

  12. Kohavi, R., Deng, A., Frasca, B., Longbotham, R., Walker, T., Xu, Y.: Trustworthy online controlled experiments: five puzzling outcomes explained. Proceedings of the 18th Conference on Knowledge Discovery and Data Mining. http://bit.ly/expPuzzling (2012)

  13. Kohavi, R., Deng, A., Frasca, B., Walker, T., Xu, Y., Pohlmann, N.: Online controlled experiments at large scale. KDD 2013: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. http://bit.ly/ExPScale (2013)

  14. Kohavi, R., Deng, A., Longbotham, R., Xu, Y.: Seven rules of thumb for web site. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD ‘14). http://bit.ly/expRulesOfThumb (2014)

  15. Kohavi, R., Longbotham, R.: Online controlled experiments and A/B tests. In: Encyclopedia of Machine Learning and Data Mining, by Claude Sammut and Geoffrey I Webb. Springer, New York (2017)

    Google Scholar 

  16. Kohavi, R., Longbotham, R.: Unexpected results in online controlled experiments.” SIGKDD Explorations, Dec. http://bit.ly/expUnexpected (2010)

  17. Kohavi, R., Thomke, S.: The surprising power of online experiments: getting the most out of A/B and other controlled tests. Har. Bus. Rev. (Sept-October). 95(5), 74–92 (2017)

    Google Scholar 

  18. Kohavi, R., Tang, D., Xu, Y.: Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press (2020). ISBN: 1108724264

    Google Scholar 

  19. Kohavi, R., Longbotham, R., Sommerfield, D., Henne, R.M.: Controlled experiments on the web: survey and practical guide. Data Min. Knowl. Disc. 18, 140–181 (2009b). http://bit.ly/expSurvey.

    Article  MathSciNet  Google Scholar 

  20. Kohavi, R., Crook, T., Longbotham, R.: Online experimentation at Microsoft. In: Third Workshop on Data Mining Case Studies and Practice Prize. Association for Computing Machinery, Inc. (ACM), New York (2009a)

    Google Scholar 

  21. Siroker, D., Koomen, P.: A/B Testing: The Most Powerful Way to Turn Clicks into Customers. Wiley, Hoboken (2013)

    Google Scholar 

  22. Tang, D., Agarwal, A., O’Brien, D., Meyer, M.: Overlapping experiment infrastructure: more, better, faster experimentation. Proceedings 16th Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, Inc. (ACM), New York (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kohavi, R. (2019). Online Controlled Experiments at Large Scale in Society 5.0. In: Fathi, M., Khakifirooz, M., Pardalos, P.M. (eds) Optimization in Large Scale Problems. Springer Optimization and Its Applications, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-030-28565-4_4

Download citation

Publish with us

Policies and ethics