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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. Lange, Fabian & Schlosser, Rainer, 2025. "Dynamic pricing with waiting and price-anticipating customers," Operations Research Perspectives, Elsevier, vol. 14(C).

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