Professor Shriver is a quantitative marketing researcher whose interests include online privacy, network effects, multi-channel demand, sustainability, empirical industrial organization and applied econometrics.
keywords
online privacy, network effects, online social networks, technology adoption, new product development, multi-channel demand, alternative energy, green marketing, social enterprise, empirical industrial organization, applied econometrics
Teaching
courses taught
MKTG 3050 - Customer Analytics
Primary Instructor
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Spring 2022 / Spring 2023
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.
MKTG 7840 - Quantitative Marketing Seminar 1
Primary Instructor
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Spring 2021
Provides a foundation for quantitative analysis in marketing. The empirical component covers fundamental empirical modeling techniques (e.g., field experiments, diffusion models, categorical data models, consumer heterogeneity). The theoretical components illustrates how utility maximizing consumers learn about consumption environment and respond to firms' marketing decisions and examines firms' competitive strategy and marketing mix decisions and relevant organizational and sociological factors.
MSBX 5310 - Customer Analytics
Primary Instructor
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Spring 2018 / Spring 2019 / Spring 2020 / Spring 2021 / Spring 2022 / Spring 2023
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.
