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Practice paper

Marketing analytics in the age of machine learning

David Booth
Applied Marketing Analytics: The Peer-Reviewed Journal, 4 (3), 214-221 (2019)
https://doi.org/10.69554/KRMG2945

Abstract

Machine learning presents unique challenges and tremendous opportunities for today’s marketer, and while many applications have already become common practice, the future holds exciting use cases, some of which are in development and others yet to be imagined. Leveraging the vast amount of data available in the exhaust stream of digital marketing and advertising, and coupling this with almost limitless data storage and processing capacity, the move from rules-based to intelligent analysis is driving efficiencies across a number of marketing initiatives and capabilities. From intelligent bidding and the serving of advertisements across the most common digital channels to advanced segmentation, audience creation, attribution and more, machine learning has already established itself across a large and complex marketing ecosystem. Recent applications in purchase intent and churn modelling, data-driven retargeting and even data-driven creative are using machine learning to provide competitive advantage now and into the future.

Keywords: artificial intelligence; machine learning; marketing analytics; predictive; digital analytics; marketing technology

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Author's Biography

David Booth is a founding partner at leading data and analytics firm Cardinal Path, as well as an author, instructor, adjunct professor and public speaker. He has helped organisations worldwide use data and analytics to empower confident business decisions, and was named the Digital Analytics Association’s 2014 Practitioner of the Year. David earned his master of business administration in international management from the Monterey Institute of International Studies and holds a BS in electrical and computer engineering from the University of Illinois at Urbana-Champaign.

Citation

Booth, David (2019, February 6). Marketing analytics in the age of machine learning. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 4, Issue 3. https://doi.org/10.69554/KRMG2945.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 4 / Issue 3
© Henry Stewart
Publications LLP

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