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Marketing and Data Science: Together the Future is Ours

Author

Listed:
  • Chintagunta Pradeep

    (Joseph T. and Bernice S. Lewis Distinguished Service Professor of Marketing, Booth School of Business, University of Chicago, Chicago, United States of America)

  • Hanssens Dominique M.

    (Distinguished Research Professor of Marketing, UCLA Anderson School of Management, University of California Los Angeles, United States of America)

  • Hauser John R.

    (Kirin Professor of Marketing, MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, United States of America)

Abstract

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data.

Suggested Citation

  • Chintagunta Pradeep & Hanssens Dominique M. & Hauser John R., 2016. "Marketing and Data Science: Together the Future is Ours," NIM Marketing Intelligence Review, Sciendo, vol. 8(2), pages 18-23, November.
    • Handle: RePEc:vrs:gfkmir:v:8:y:2016:i:2:p:18-23:n:2
    DOI: 10.1515/gfkmir-2016-0011
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    Cited by:

    1. Noble, Stephanie M. & Mende, Martin & Grewal, Dhruv & Parasuraman, A., 2022. "The Fifth Industrial Revolution: How Harmonious Human–Machine Collaboration is Triggering a Retail and Service [R]evolution," Journal of Retailing, Elsevier, vol. 98(2), pages 199-208.
    2. Jacek Maślankowski, 2017. "Automatic Analysis of Unstructured Content as an Example of a Data Source for the Public Administration," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 46, pages 161-172.

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