Marketing mix modelling 4.0: The superiority of agentic, Bayesian optimised marketing mix modelling over traditional approaches
Abstract
This research paper explores how an agentic, Bayesian optimisation driven marketing mix modelling (MMM) framework offers significant advantages over traditional, static regression-based methods, advancing MMM from its econometric roots to better meet the needs of modern marketers. Traditional MMM often relies on linear regression with predefined transformations and fixed decay rates, leading to sub-optimal parameter estimations and limited predictive power. Our proposed agentic approach leverages Bayesian optimisation to dynamically discover optimal adstock (carryover) and diminishing returns (saturation) parameters for each marketing channel. This data-driven, adaptive parameter tuning, combined with robust feature engineering for seasonality and automated model logging and drift detection, results in more accurate sales attribution, improved return on advertising spend (ROAS) predictions and enhanced strategic budget allocation. This paper details the methodological advancements, presents the underlying mathematical formulations, and discusses the practical implications and benefits for marketing practitioners seeking to maximise their marketing effectiveness. 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
David J. Fogarty David Fogarty has over 35 years of industry experience leading large analytics teams for Fortune 500 firms and has lectured on business analytics at Columbia University, Wharton, University of Liverpool, New York University and Cornell University. He currently leads the Data Excellence and Privacy Practice at the Association of National Advertisers and is focused on collaborating with the leading academics, client-side marketers and marketing service providers to advance the science and practice of marketing mix modelling and other data science techniques applied to marketing.
Saumitra Bhaduri Saumitra N. Bhaduri is Professor of Financial Economics and Econometrics at the Madras School of Economics. He holds an MA in econometrics from the University of Calcutta and a PhD in financial economics from the Indira Gandhi Institute of Development Research. Before joining academia, he worked as a quantitative analyst at General Electric (GE) India, where he founded and led the GE–MSE Decision Sciences Laboratory. His research interests include corporate finance, financial markets in emerging economies, behavioural finance and advanced quantitative methods. Dr Bhaduri has authored various scholarly articles and co-edited the book ‘Advanced Business Analytics: The Creation of Competitive Advantage’ (Springer, 2016).
Ranganathan Srinivasan is the Founder and CEO of EKO Infomatics Solutions and has over 25 years of experience leading global initiatives in artificial intelligence (AI), machine learning and decision science. He holds a master’s degree in statistics from the Indian Statistical Institute and specialises in developing and deploying scalable AI solutions across industries. He actively collaborates with academic and research institutions to translate advanced AI methodologies into practical, high-impact enterprise applications.
Citation
Fogarty, David J., Bhaduri, Saumitra and Srinivasan, Ranganathan (2025, December 1). Marketing mix modelling 4.0: The superiority of agentic, Bayesian optimised marketing mix modelling over traditional approaches. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 11, Issue 3. https://doi.org/10.69554/ZPRN1965.
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