Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices

51 Pages Posted: 16 Apr 2024 Last revised: 13 Jun 2024

See all articles by Ryan Dew

Ryan Dew

University of Pennsylvania - Marketing Department

Nicolas Padilla

London Business School - Department of Marketing

Lan E. Luo

Columbia University - Columbia Business School

Shin Oblander

Columbia University - Columbia Business School, Marketing

Asim Ansari

Columbia University - Columbia Business School, Marketing

Khaled Boughanmi

Cornell University - Samuel Curtis Johnson Graduate School of Management

Michael Braun

Southern Methodist University (SMU) - Marketing Department

Fred M. Feinberg

University of Michigan at Ann Arbor - Marketing; University of Michigan, Stephen M. Ross School of Business

Jia Liu

HKUST Business School

Thomas Otter

Goethe University Frankfurt - Department of Marketing

Longxiu Tian

University of North Carolina at Chapel Hill

Yixin Wang

University of Michigan

Mingzhang Yin

University of Florida - Warrington College of Business Administration

Date Written: April 10, 2024

Abstract

Making sense of massive, individual-level data is challenging: marketing researchers and analysts need flexible models that can accommodate rich patterns of heterogeneity and dynamics, work with and link diverse data types, and scale to modern data sizes. Practitioners also need tools that can quantify uncertainty in models and predictions of consumer behavior to inform optimal decision-making. In this paper, we demonstrate the promise of probabilistic machine learning (PML), which refers to the pairing of probabilistic modeling and machine learning methods, in pushing the frontier of combining flexibility, scalability, interpretability, and uncertainty quantification for building better models of consumers and their choices. Specifically, we overview both PML models and inference methods, and highlight their utility for addressing four common classes of marketing problems: (1) uncovering heterogeneity, (2) flexibly modeling nonlinearities and dynamics, (3) handling high-dimensional and unstructured data, and (4) addressing missingness, often via data fusion. We also discuss promising directions in enriching marketing models, reflecting recent developments in representation learning, causal inference, experimentation and decision-making, and theory-based behavioral modeling.

Keywords: machine learning, Bayesian statistics, Bayesian nonparametrics, generative models, unstructured data, representation learning, causal inference

Suggested Citation

Dew, Ryan and Padilla, Nicolas and Luo, Lan E. and Oblander, Shin and Ansari, Asim and Boughanmi, Khaled and Braun, Michael and Feinberg, Fred M. and Feinberg, Fred M. and Liu, Jia and Otter, Thomas and Tian, Longxiu and Wang, Yixin and Yin, Mingzhang, Probabilistic Machine Learning: New Frontiers for Modeling Consumers and their Choices (April 10, 2024). The Wharton School Research Paper, Available at SSRN: https://ssrn.com/abstract=4790799 or http://dx.doi.org/10.2139/ssrn.4790799

Ryan Dew (Contact Author)

University of Pennsylvania - Marketing Department ( email )

700 Jon M. Huntsman Hall
3730 Walnut Street
Philadelphia, PA 19104-6340
United States

Nicolas Padilla

London Business School - Department of Marketing ( email )

Sussex Place
Regent's Park
London, NW1 4SA
United Kingdom

HOME PAGE: http://www.nicolaspadilla.com

Lan E. Luo

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

Shin Oblander

Columbia University - Columbia Business School, Marketing ( email )

New York, NY 10027
United States

HOME PAGE: http://www.shin.marketing

Asim Ansari

Columbia University - Columbia Business School, Marketing ( email )

New York, NY 10027
United States

Khaled Boughanmi

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

Michael Braun

Southern Methodist University (SMU) - Marketing Department ( email )

United States

Fred M. Feinberg

University of Michigan at Ann Arbor - Marketing ( email )

Ann Arbor, MI 48109
United States

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

Jia Liu

HKUST Business School ( email )

Clear Water Bay
Hong Kong

Thomas Otter

Goethe University Frankfurt - Department of Marketing ( email )

Frankfurt
Germany
++49.69.798.34646 (Phone)

HOME PAGE: http://www.marketing.uni-frankfurt.de/index.php?id=97?&L=1

Longxiu Tian

University of North Carolina at Chapel Hill ( email )

Chapel Hill, NC 27599
United States

Yixin Wang

University of Michigan ( email )

Ann Arbor, MI
United States

Mingzhang Yin

University of Florida - Warrington College of Business Administration ( email )

Gainesville, FL 32611
United States

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