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Journal of Marketing Analytics

, Volume 6, Issue 2, pp 62–68 | Cite as

User-level incremental conversion ranking without A/B testing

  • Zhuli Xie
  • Yong Liu
Original Article

Abstract

The traditional approach to build incremental conversion prediction model has to rely on A/B test results. However, under certain intense business environment, A/B testing can be limited by technical support, platform, or budget, and become not practically available. In this paper, we propose an algorithm to rank users by incremental conversions resulted from advertising effects, which is based on user’s conversion history and the output from a conversion prediction model. By appropriately defining who an active user is, this algorithm is proven to work well with real data. In case where an A/B test is not available and incremental conversion-based user targeting is desired, this algorithm offers a practical solution.

Keywords

User targeting Incremental conversion Machine learning Business intelligence Big data 

Notes

Acknowledgements

We are grateful to the support of Oath field marketing data science and analytics organization for this study. It is a great pleasure for Y. Liu to dedicate this paper to his Ph.D. supervisor, Prof. Mo-Lin Ge, on the occasion of his eightieth birthday.

References

  1. Bawa, K., and R.W. Shoemaker. 1989. Analyzing incremental sales from a direct mail coupon promotion. Journal of Marketing 53 (July 1989): 66–78.CrossRefGoogle Scholar
  2. Bell, R., Y. Koren, and C. Volinsky. 2007. Modeling relationships at multiple scales to improve accuracy of large recommender systems, in Proceedings of KDD’07, pp. 95–104, San Jose, California, USA, 2007.Google Scholar
  3. David, X., Y. Yuan, J. Koehler, and D. Kumar. 2011. Incremental clicks the impact of search advertising. Journal of Advertising Research 51 (4): 643–647.CrossRefGoogle Scholar
  4. Evans, D.S. 2009. The online advertising industry: Economics, evolution, and privacy. The Journal of Economic Perspectives 23 (3): 37–60.CrossRefGoogle Scholar
  5. Gordon, B., F. Zettelmeyer, N. Bhargava, and D. Chapsky. 2016. http://www.kellogg.northwestern.edu/faculty/gordon_b/files/fb_comparison.pdf.
  6. Johnson, G.A., and R.A. Lewis. 2015. Cost Per Incremental Action: Efficient Pricing of Advertising, Simon Business School Working Paper No. FR 15-29, Available at SSRN: https://ssrn.com/abstract=2668315 or http://dx.doi.org/10.2139/ssrn.2668315.
  7. Kumar, S. Optimization Issues in Web and Mobile Advertising, in SpringerBriefs in Operations Management, (New York: Springer, 2015).  https://doi.org/10.1007/978-3-319-18645-0_2.
  8. Li, H., and P.K. Kannan. 2014. Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research 51 (1): 40–56.CrossRefGoogle Scholar
  9. Liu, Y., J. Laguna, M. Wright, and H. He. 2014. Media mix modeling—A Monte Carlo simulation study. Journal of Marketing Analytics 2 (3): 173–186.CrossRefGoogle Scholar
  10. Pechyony, D., R. Jones, X. Li, and D. Cerrato. A joint optimization of incrementality and revenue to satisfy both the advertiser and the publisher/ad network, https://pdfs.semanticscholar.org/0fac/cd336b65d7c512626280e41f5f581621c0f0.pdf.
  11. Shao, X., and L. Li. 2011. Data-driven multi-touch attribution models, in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 258–264.Google Scholar
  12. Spark Machine Learning Library (MLlib) Guide. 2017. https://spark.apache.org/docs/2.1.0/ml-guide.html.

Copyright information

© Macmillan Publishers Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Applied Data Science and AnalyticsOath Inc.SunnyvaleUSA

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