This course examines the fundamental issues of creating a strategy for monetization and revenue growth within an organization. Students learn about setting an organization's business model design, aligning various functional areas within the company to implement a monetization strategy, and the tradeoffs that occur when choosing amongst profitable monetization policies for the firm. They master concepts, frameworks, and tools to assess an industry and a firm's pricing strategy and business models, and to craft alternatives. They also study the interplay between marketing, salesforces, HR incentives and human capital management, advertising and data and analytics in shaping a winning monetization policy. Topics we will cover include monetizing online content and strategies in ad-driven industries, understanding freemium models and installed-base competition, monetization of consumer data, privacy considerations and the privacy economy, business models from the perspective of investors and venture capitalists, regulatory considerations, and linking monetization to the ability to measure and capture value. We will use a mix of cases and lectures along with extensive participation from industry leaders to bring to light the various issues in class.
This class will provide an overview of the travel and hospitality industry focusing on strategy, business models, institutions and innovations. Issues we will cover include completion issues, operational considerations, service quality assessment, promotion management and the use of analytics within verticals such as airlines, hotels, casinos and cruise lines. We will also discuss new innovations such as shared consumption models and the role of online reviews and user generated content in facilitating travel. The class will involve a mix of cases and lectures; site visits to Bay Area firms and interactions with several industry leaders in the travel space.
This course will focus on empirical tools for analyzing dynamic decision contexts, wherein current actions of firms or consumers have effects on future payoffs, profits and/or competitive conduct. The course will build the relevant material generally, but our applications will be mostly focused on empirical marketing and industrial organization problems. We will have an applied focus overall, emphasizing the practical aspects of implementation, especially programming.The overall aim of the class is to help students obtain the skills to implement these methods in their research. By the end of the class, students are expected to be able to formulate a dynamic decision problem, program it in a language such as Matlab or C, and to estimate the model from data. The course starts with an overview of consumer theory and static models of consumer choice. We build on this material and introduce discrete choice markovian decision problems, and continuous markovian decision problems, and focus on building the computational toolkit for the numerical analysis of these problems. We then move on to specific applications, and discuss multi-agent dynamic equilibrium models. Finally, we discuss recently proposed advanced methods for alleviating computational burden in dynamic models.
Pricing right is fundamental to a firm’s profitability in a competitive business environment. Yet firms in diverse industries implement ad-hoc rules and trial-and-error approaches to pricing that significantly reduce profits. This course will draw on analytic marketing techniques, marketing strategy, and microeconomic theory to describe practical approaches that are useful for optimal pricing decision-making. The main objective is to help students develop a systematic framework to think about, analyze and develop strategies for pricing right. Some of the questions we will address in the course include: How does a firm determine the price of a new product? How does a firm assess whether the current price is appropriate? What is value pricing? How does one implement it? What is price segmentation? A combination of cases, lectures, and empirical applications will be used in the class. The course is aimed at students who will, in their careers, be involved with formulating, analyzing and/or recommending pricing polices in the context of an integrated business strategy for the firm. Students with an understanding of marketing and microeconomic principles will benefit most from the course.
Data and Decisions is a first-year core MBA course in probabaility, statistics and decision analysis. This course introduces the fundamental concepts and techniques for analyzing risk and formulating sound decisions in uncertain environments. The goal is to teach students how to evaluate quantitative information and to make sound decisions in complex situations. Approximately half of the course focuses on probability theory and decision analysis, including decision trees, decision criteria, the value of information, and simulation techniques. The remainder of the course examines statistical methods for interpreting and analyzing data including sampling concepts, regression analysis, and hypothesis testing. Applications include inventory management, demand analysis, lotteries and gambling, portfolio analysis, insurance, auctions, surveys and opinion polls, environmental contamination, failure analysis and quality control. The course emphasizes analytical techniques and concepts that are broadly applicable to business problems.