Why domain knowledge is essential for data scientists in marketing

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, 2022, vol. 7, issue 4, 362-373

Abstract: What good is the most scientifically valuable analysis if it piles up in marketers’ inboxes but does not give them the necessary foundation for their decisions? Such a situation is no use to data scientists and certainly no use to the marketing team. The root of the issue is that two worlds meet here that speak completely different languages. Only if data scientists can ‘translate’ their results into marketing language will their work be successful. Marketing teams do not need as much information as possible; rather, they require just the right information, preferably with recommendations for action that can guide their decisions. To select the information that is truly useful for marketing and communicate it in an understandable way, data scientists must have more than expertise in analytics methods and tools (which is assumed and therefore not discussed in detail here); they also need to know about marketing objectives and have a comprehensive contextual understanding of their company’s industry and sector, including competitors. Knowledge of the general situation in the world as well as the legal, political and religious particularities of the countries in which the company operates is also required. In short, analytics results that truly drive marketing can only be delivered by data scientists with domain knowledge. Using a case study from the field, this paper shows how data scientists can gain the domain knowledge they need to be successful in marketing and in which aspects of their work it helps them perform more effectively.

Keywords: domain knowledge in data science; marketing analytics; success factors; data scientist; predictive modelling; statistics; computer science (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2022
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