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Teaching marketing analytics: a pricing case study for quantitative and substantive marketing skills

Author

Listed:
  • Bernd Skiera

    (Goethe University Frankfurt)

  • Lukas Jürgensmeier

    (Goethe University Frankfurt)

Abstract

This article describes a data-driven case study for teaching and assessing students’ skills in marketing analytics, specifically in pricing. This case study combines teaching econometrics to analyze data and substantive marketing to derive managerial insights. The econometric challenge requires students to set up and implement a regression analysis to derive the demand function, detect multicollinearity, and select appropriate data visualizations. The substantive challenge requires deriving optimal pricing decisions and understanding how the parameters of the demand function impact optimal prices and the associated profit. We test the case study in a marketing analytics exam and discuss the performance of 134 students. Beyond assessing student performance in an exam, the case study facilitates teaching through in-class group work or assignments. Free of charge, under a liberal CC BY license, we encourage other educators to use the case study in their teaching. We provide the necessary data and a sample solution using the statistical programming language R.

Suggested Citation

  • Bernd Skiera & Lukas Jürgensmeier, 2024. "Teaching marketing analytics: a pricing case study for quantitative and substantive marketing skills," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 209-226, June.
    • Handle: RePEc:pal:jmarka:v:12:y:2024:i:2:d:10.1057_s41270-024-00313-2
    DOI: 10.1057/s41270-024-00313-2
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    References listed on IDEAS

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    1. Matthew J. Kotchen & James H. Stock & Catherine D. Wolfram, 2019. "Introduction to "Environmental and Energy Policy and the Economy"," NBER Chapters, in: Environmental and Energy Policy and the Economy, volume 1, pages 3-7, National Bureau of Economic Research, Inc.
    2. Maria Petrescu & Anjala S. Krishen, 2018. "Novel retail technologies and marketing analytics," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(3), pages 69-71, September.
    3. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.
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    Cited by:

    1. Jürgensmeier, Lukas & Skiera, Bernd, 2024. "Generative AI for scalable feedback to multimodal exercises," International Journal of Research in Marketing, Elsevier, vol. 41(3), pages 468-488.

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