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Big Data-Driven Marketing: How Machine Learning Outperforms Marketers’ Gut-Feeling

  • Pål Sundsøy
  • Johannes Bjelland
  • Asif M. Iqbal
  • Alex “Sandy” Pentland
  • Yves-Alexandre de Montjoye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

This paper shows how big data can be experimentally used at large scale for marketing purposes at a mobile network operator. We present results from a large-scale experiment in a MNO in Asia where we use machine learning to segment customers for text-based marketing. This leads to conversion rates far superior to the current best marketing practices within MNOs.

Using metadata and social network analysis, we created new metrics to identify customers that are the most likely to convert into mobile internet users. These metrics falls into three categories: discretionary income, timing, and social learning. Using historical data, a machine learning prediction model is then trained, validated, and used to select a treatment group. Experimental results with 250 000 customers show a 13 times better conversion-rate compared to the control group. The control group is selected using the current best practice marketing. The model also shows very good properties in the longer term, as 98% of the converted customers in the treatment group renew their mobile internet packages after the campaign, compared to 37% in the control group. These results show that data-driven marketing can significantly improve conversion rates over current best-practice marketing strategies.

Keywords

Marketing Big Data Machine learning social network analysis Metadata Asia Mobile Network Operator Carrier 

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References

  1. 1.
    Chinn, M.D., Fairlie, R.W.: The Determinants of the Global Digital Divide: A Cross- Country Analysis of Computer and Internet Penetration. Economic Growth Center (2004)Google Scholar
  2. 2.
  3. 3.
    de Montjoye, Y.-A., Quoidbach, J., Robic, F., Pentland, A(S.): Predicting Personality Using Novel Mobile Phone-Based Metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 48–55. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  4. 4.
    Staiano, J., Lepri, B., Aharony, N., Pianesi, F., Sebe, N., Pentland, A.: Friends don’t lie: inferring personality traits from social network structure. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 321–330 (2012)Google Scholar
  5. 5.
    Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Ramones, S.M., et al.: Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach. PLoS One 8(9), e73791 (2013), doi:10.1371/journal.pone.0073791CrossRefGoogle Scholar
  6. 6.
    Jernigan, C., Mistree, B.F.T.: Gaydar: Facebook friendships expose sexual orientation. First Monday, [S.l.] (2009), http://journals.uic.edu/ojs/index.php/fm/article/view/2611/2302, ISSN 13960466
  7. 7.
    Backstrom, L., Kleinberg, J.: Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook, arXiv preprint arXiv:1310.6753 (2013)Google Scholar
  8. 8.
    Bond, R.M., Fariss, C.J., Jones, J.J., Kramer, A.D.I., Marlow, C., Settle, J.E., Fowler, J.H.: A 61-million-person experiment in social influence and political mobilization. Nature 489(7415), 295 (2012), doi:10.1038/nature11421CrossRefGoogle Scholar
  9. 9.
    Aral, S., Walker, D.: Identifying Influential and Susceptible Members of Social Networks. Science 337(6092), 337 (2012)CrossRefMathSciNetGoogle Scholar
  10. 10.
  11. 11.
    Turner, J.C.: Social influence. Mapping social psychology series, p. 206. Thomson Brooks/Cole Publishing Co., Belmont (1991)Google Scholar
  12. 12.
    Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. New England Journal of Medicine 357(4), 370–379 (2007)CrossRefGoogle Scholar
  13. 13.
    Sundsøy, P., Bjelland, J., Engø-Monsen, K., Canright, G., Ling, R.: Comparing and visualizing the social spreading of products on a large-scale social network. In: Ozyer, T., et al. (eds.) The influence on Technology on Social Network Analysis and Mining, XXIII, 643 p. 262. Springer (2013)Google Scholar
  14. 14.
    Sundsøy, P., Bjelland, J., Canright, G., Engø-Monsen, K., Ling, R.: Product adoption networks and their growth in a large mobile phone network. IEEE Advanced in Social Network Analysis and Mining (2010)Google Scholar
  15. 15.
    Breiman: Technical Report No. 421, Department of Statistics University of California (1994)Google Scholar
  16. 16.
    de Montjoye, Y.-A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the Crowd: The privacy bounds of human mobility. Nature Scientific Reports 3, id. 1376 (2013)Google Scholar
  17. 17.
    de Montjoye, Y.-A., Wang, S., Pentland, A.: On the Trusted Use of Large-Scale Personal Data. IEEE Data Engineering Bulletin 35(4) (2012)Google Scholar
  18. 18.
    de Montjoye, Y.-A., Quoidbach, J., Robic, F., Pentland, A(S.): Predicting personality using novel mobile phone-based metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 48–55. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pål Sundsøy
    • 1
  • Johannes Bjelland
    • 1
  • Asif M. Iqbal
    • 1
  • Alex “Sandy” Pentland
    • 2
  • Yves-Alexandre de Montjoye
    • 2
  1. 1.Telenor ResearchNorway
  2. 2.The Media LaboratoryMassachusetts Institute of TechnologyUSA

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