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Methods to analyze customer usage data in a product decision process:A systematic literature review

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  • Micus, Christian
  • Schramm, Simon
  • Boehm, Markus
  • Krcmar, Helmut

Abstract

To remain competitive, companies must decide on new, desirable products. This can be achieved by integrating insights how customers use a product into the process of deciding on a new product. Currently, this process is primarily based on market research that can only reveal the intention of consumers. Through the digitization of products, companies have access to large amounts of customer data that allow the application of data analytics methods. We provide a taxonomy of artificial intelligence, machine learning and data analysis, so that the notion of data analytics can be defined. Thus, the terms customer usage data, as well as a generic, five-stage product decision process (PDP) are defined and differentiated from consumer data and the product development process. Eventually, we show which data analytics methods on customer usage data can be used in order to tackle current challenges within the PDP. We incorporate the results of our structured literature review by connecting selected examples to our concept of the PDP. Our insights help to apply the proper data analytics methods in the PDP and thereby address the interplay between product decision and product development. Finally, future research directions for data analytics methods on customer usage data are put forward.

Suggested Citation

  • Micus, Christian & Schramm, Simon & Boehm, Markus & Krcmar, Helmut, 2023. "Methods to analyze customer usage data in a product decision process:A systematic literature review," Operations Research Perspectives, Elsevier, vol. 10(C).
    • Handle: RePEc:eee:oprepe:v:10:y:2023:i:c:s221471602300012x
    DOI: 10.1016/j.orp.2023.100277
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    References listed on IDEAS

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    1. Reinartz, Werner & Wiegand, Nico & Imschloss, Monika, 2019. "The impact of digital transformation on the retailing value chain," International Journal of Research in Marketing, Elsevier, vol. 36(3), pages 350-366.
    2. Paul Klemperer, 1995. "Competition when Consumers have Switching Costs: An Overview with Applications to Industrial Organization, Macroeconomics, and International Trade," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 62(4), pages 515-539.
    3. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    4. Rehman, Muhammad Habib ur & Chang, Victor & Batool, Aisha & Wah, Teh Ying, 2016. "Big data reduction framework for value creation in sustainable enterprises," International Journal of Information Management, Elsevier, vol. 36(6), pages 917-928.
    5. Mousa Albashrawi & Hasan Kartal & Asil Oztekin & Luvai Motiwalla, 2019. "Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach," Information Systems Frontiers, Springer, vol. 21(4), pages 773-790, August.
    6. Roland T. Rust & Ming-Hui Huang, 2014. "The Service Revolution and the Transformation of Marketing Science," Marketing Science, INFORMS, vol. 33(2), pages 206-221, March.
    7. Henning Kagermann, 2015. "Change Through Digitization—Value Creation in the Age of Industry 4.0," Springer Books, in: Horst Albach & Heribert Meffert & Andreas Pinkwart & Ralf Reichwald (ed.), Management of Permanent Change, edition 127, chapter 2, pages 23-45, Springer.
    8. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    9. Sheth, Jagdish & Kellstadt, Charles H., 2021. "Next frontiers of research in data driven marketing: Will techniques keep up with data tsunami?," Journal of Business Research, Elsevier, vol. 125(C), pages 780-784.
    10. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    11. Jinyang Zheng & Zhengling Qi & Yifan Dou & Yong Tan, 2019. "How Mega Is the Mega? Exploring the Spillover Effects of WeChat Using Graphical Model," Information Systems Research, INFORMS, vol. 30(4), pages 1343-1362, December.
    12. Vasant Dhar & Tomer Geva & Gal Oestreicher-Singer & Arun Sundararajan, 2014. "Prediction in Economic Networks," Information Systems Research, INFORMS, vol. 25(2), pages 264-284, June.
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