Driving value in digital marketing: Navigating from applied machine learning to applied artificial intelligence
Abstract
Today’s digital marketing analysts must navigate evolving regulations, stringent privacy policies, an assortment of tools, a fragmented technical landscape and the rapidly evolving fields of machine learning (ML) and artificial intelligence (AI). This paper addresses these challenges by proposing a comprehensive framework that splits the digital marketer’s role into three areas: marketing strategy, marketing operations and marketing technology. Within this context, the paper reviews how digital marketing analysts currently leverage ML and analytics. It then explores recent advancements in AI that are hoped will address the shortcomings associated with current ML solutions, before turning its attention to the shortcomings associated with AI. The paper underscores how digital marketing analysts need to weave AI strategically into all aspects of their role if they are to remain competitive and relevant in the Web3 era. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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Author's Biography
Vidya Subramanian is a passionate leader in the field of data science and analytics, with experience leading teams at Google, Apple and Intuit. She has previously been recognised by Forbes as one of the ‘8 Female Analytics Experts From The Fortune 500’. She is the author of ‘Applied Machine Learning for Data Science Practitioners’ (Wiley), ‘Adobe Analytics with SiteCatalyst’ (Adobe Press), and ‘McGraw- Hill’s PMP Certification Mathematics’ (McGraw-Hill), among other publications. As a thought leader, she frequently presents at industry conferences. Vidya holds master’s degrees from Virginia Tech and the Somaiya Institute of Management. She currently leads Data Science and Analytics for Google Play.
Citation
Subramanian, Vidya (2025, September 1). Driving value in digital marketing: Navigating from applied machine learning to applied artificial intelligence. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 11, Issue 2. https://doi.org/10.69554/NDMY9137.Publications LLP