PhDs.io — Knowledge that Transforms

Does Access to Human Coaches Lead to More Weight Loss Than With AI Coaches Alone?

Anuj Kapoor; Sridhar Narayanan; Puneet Manchanda

Marketing Science 2026

Access to human coaches leads to more weight loss than with access to an AI coach alone, with consumers setting higher weight loss goals, tracking their weight and food intake more frequently.

On the Value of Coalition Loyalty Programs

Yan Liu; Jingmai Wang; Yulan Wang; Dan Zhang

Marketing Science 2026

This paper examines how coalition loyalty programs enhance firms’ ability to price discriminate and shows why the optimal program size depends on market composition.

An Empirical Analysis of Optimal Nonlinear Pricing in Business-to-Business Markets

Soheil Ghili; Russ Yoon

Marketing Science 2026

This paper provides a methodology for business-to-business firms to implement nonlinear pricing optimally.

Focus on Authors

Marketing Science 2026

Leveraging Generative Artificial Intelligence to Create Visual Content in Digital Advertising

Remi Daviet; Yohei Nishimura

Marketing Science 2026

Creating high-performing, brand-aligned advertising visuals is challenging; we develop a generative AI framework using Bayesian active learning to efficiently discover effective designs.

Talking Without Speaking: Paid Trolls on Social Media and Court Decision

Liu Yi; Haitao (Tony) Cui

Marketing Science 2026

This article aims to investigate how public opinions and paid trolls may affect the outcome of legal disputes between opposing parties.

The Ripple Effect of Platform Quality Controls in a Submarket: Evidence from a Secondhand Marketplace

Feng Yu; Hui Li; Qiaowei Shen

Marketing Science 2026

Platform quality controls within a submarket have a positive carryover effect on adopters and a U-shaped spillover effect on nonadopters.

Unraveling Multifaceted User Preferences on Digital Platforms: A Bayesian Deep-Learning Approach

Mingzhang Yin; Ziwei Cong; Jia Liu

Marketing Science 2026

This paper proposes a Bayesian deep-learning model that captures multifaceted, time-varying user activities on digital platforms, yielding interpretable and dynamic preference estimations at the platform and individual levels.

Privacy-Preserving Data Fusion

Longxiu Tian; Dana Turjeman; Samuel Levy

Marketing Science 2026

This paper proposes a generative methodology enabling companies to combine data sets with strong privacy guarantees and near-optimal performance in customer analytics.

A Bayesian Dual Clustering Approach for Selecting Data and Parameter Granularities

Mingyung Kim; Eric T. Bradlow; Raghuram Iyengar

Marketing Science 2026

We propose a Bayesian dual clustering method that infers both data and parameter granularities.

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