Research in operations management has traditionally focused on models for understanding,
mostly at a strategic level, how firms should operate. Spurred by the growing availability of …

The widespread impacts of artificial intelligence (AI) and machine learning (ML) in many
segments of society have not yet been felt strongly in the marketing field. Despite such …

In many settings, a decision maker wishes to learn a rule, or policy, that maps from
observable characteristics of an individual to an action. Examples include selecting offers …

We study a completion problem of broad practical interest: the reconstruction of a low-rank
symmetric tensor from highly incomplete and randomly corrupted observations of its entries …

Predictive analytics is increasingly used to guide decision making in many applications.
However, in practice, we often have limited data on the true predictive task of interest and …

This chapter reviews the recent developments at the intersection of personalization and AI in
marketing and related fields. We provide a formal definition of personalized policy and …

This paper studies offline policy learning, which aims at utilizing observations collected a
priori (from either fixed or adaptively evolving behavior policies) to learn the optimal …

Consider a setting with $ N $ heterogeneous units (eg, individuals, sub-populations) and $
D $ interventions (eg, socio-economic policies). Our goal is to learn the expected potential …

Multiple criteria approaches can assist the product manager to know the consumer
preferences in the context of e-commerce. Consumer preference analysis explains what …

In a wide variety of applications, including healthcare, bidding in first price auctions, digital
recommendations, and online education, it can be beneficial to learn a policy that assigns …