My research focuses on how firms can use individual longitudinal choice data to increase engagement, usage, and spending in experiential categories. The importance of experiential consumption among the consumer base is growing, along with its economic significance: 74% of Americans claim to prioritize experiences over ownership of material goods, and they spend billions of dollars in these categories, e.g., over $55B on sports events alone in the U.S. in 2017. My current projects span two broad experiential domains: live events (art performances and professional sports) and charitable donation. In close collaboration with industry partners, I study how to leverage past trajectories of consumer choices to optimize marketing activities, including product recommendation, price and inventory management, and donation solicitation. In these projects, I use both observational and field experiment data, and a range of estimation methodologies, primarily hierarchical and nonparametric Bayesian, and increasingly tools from machine learning.
MKTG 3050 - Customer Analytics
Primary Instructor
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Fall 2024
Students develop a deep understanding of customer centricity and its implications for the firm, learn about state-of-the-art methods for calculating customer lifetime value and customer equity and develop the analytical and empirical skills that are needed to judge the appropriateness, performance and value of different statistical techniques that can be used to address issues around customer acquisition, development and retention.
MSBX 5310 - Customer Analytics
Primary Instructor
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Spring 2024
Provides a deep understanding of how to use data on customer behavior and preferences to inform managerial decision making. Introduces methods for causal inference, modeling consumer demand, and modeling firm decisions. Applications include long-run customer management decisions (customer acquisition and retention) and short-run marketing mix (product, price, promotion and distribution) decisions. The R programming language is used for course examples and assignments. Students are assumed to have a working knowledge of R and linear regression techniques.
