The importance of domain knowledge for successful and robust predictive modelling

Andrea Ahlemeyer-Stubbe and Agnes Müller
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Andrea Ahlemeyer-Stubbe: Director Strategic Analytics, servicepro GmbH, Germany
Agnes Müller: Senior Analytical Consultant, servicepro GmbH, Germany

Applied Marketing Analytics: The Peer-Reviewed Journal, 2021, vol. 6, issue 4, 344-352

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.

Keywords: predictive modelling; domain knowledge; binning; dummy variables; data preparation; missing data; data mining (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2021:v:6:i:4:p:344-352

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