Pricing is one of the most powerful levers that a firm can use to maximize profits. However, this opportunity to impact profits often remains untapped as many managers do not know how to improve upon historical pricing practices within their companies and industries. This course prepares students to address both strategic and tactical pricing issues and to identify profit-boosting changes in pricing practices across a range of professional contexts – as management consultants, product managers, entrepreneurs, business-unit managers and M&A advisors.

The course is structured around three modules. These are

1. Pricing Analysis:  In this first module, you will use a variety of data and statistical models to analyze demand and assess willingness to pay. You will also learn how to measure price sensitivity and how to compute price elasticities for new products and for more established ones.
2. Price Structures and Metrics: In this module, which forms the heart of the course, you will learn when to use different price structures and how to design them optimally. These price structures take the form of product-line pricing, segmented pricing, price bundling, non-linear price schedules, subscription pricing and other forms of targeting. You will also learn how to use appropriate pricing metrics to monetize value.
3. Price Management: In this final module you will study how to manage prices via pricing policy, price delegation, markdown pricing, revenue management and price promotions.

The course uses a mix of lectures, case discussions, mini-cases, analytical exercises, and guest speakers and covers pricing across a broad spectrum of industries involving both B2C and B2B and from both economic and psychological perspectives.

Asim Ansari

William T. Dillard Professor of Marketing

Professor Ansari's research addresses customer relationship management, e-commerce personalization and targeting, social network modeling, and Bayesian models of consumer actions. He is currently working on the use of machine learning methods for Big-Data settings in marketing. Prior...

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