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The importance of domain knowledge for successful and robust predictive modelling

Author

Listed:
  • Ahlemeyer-Stubbe, Andrea

    (Director Strategic Analytics, servicepro GmbH, Germany)

  • Müller, Agnes

    (Senior Analytical Consultant, servicepro GmbH, Germany)

Abstract

Domain knowledge helps to build more precise and robust predictive models and thus obtain better insights. In the course of preparatory work, it helps inform what questions to ask, define the key fields to examine more closely, and identify where and how the insights from the analysis can support business goals. As this paper will discuss, it is also of great benefit when it comes to selecting or reducing variables, supplementing missing data, handling outliers or applying specific binning techniques. This paper argues that data scientists cannot rely on technical knowledge alone; rather, they must acquire relevant domain knowledge and familiarise themselves with pertinent rules of thumb. The paper also highlights the importance of maintaining close contact with the people who collect and prepare the data.

Suggested Citation

  • Ahlemeyer-Stubbe, Andrea & Müller, Agnes, 2021. "The importance of domain knowledge for successful and robust predictive modelling," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 6(4), pages 344-352, March.
    • Handle: RePEc:aza:ama000:y:2021:v:6:i:4:p:344-352
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    More about this item

    Keywords

    predictive modelling; domain knowledge; binning; dummy variables; data preparation; missing data; data mining;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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