Revenue Management and Pricing Analytics

We first provide a general overview of the field ofrevenue management and pricing (PRO Chapter 1 and 2, TVR Chapter 1). We then proceed with three parts: Quantity-based revenue management, Customer-choice modelling, and Price-based revenue management.

Quantity-based revenue management (Week 1 to Week 4*)*

• Booking limits for single-resource capacity control: Littlewood’s two-class model, n-class models,EMSR-A and EMSR-B heuristics (PRO Chapter 6 and 7, TVR Chapter 2)
• Intro to dynamic programming (Handout)
• Network capacity control for multiple resources (PRO Chapter 8)
• Overbooking models (PRO Chapter 9, TVR Chapter 4)

Customer-choice modelling(Week 4)

• Price-response functions, Reservation price models, random-utility models (PRO Chapter 3, TVR Chapter 7)

Price-based revenue management (Week 5 to Week 7)

• Static price optimization (PRO Chapter 3)
• Dynamic pricing (TVR Chapter 5)
• Markdown pricing (PRO Chapter 10)

Recap of all material (Week 8)

Additional information Tests
Project:
What will be assessed?
By translating a business case into an appropriate revenue management model, and applying it to real data, four objectives of the course will be assessed:

1. understand quantity-based and price-based revenue management models;
2. construct the appropriate revenue management model for a given situation in practice;
3. applyand implement (in a programming language) quantitative models and optimization procedures to solve companies’ revenue optimization problems based on data;
4. Identity opportunities for revenue optimization of companies from various industries.

Remark:
Students will work in groups of maximum 5 on real-life applications of revenue management and pricing analytics. Both aquantity-based and price-based revenue management model that is taught in the class will be implemented in a programming language, and evaluated using real data.

Written examination:
Students will be assessed whether they:

1. understand quantity-based and price-based revenue management models;
2. construct the appropriate revenue management model for a given situation in practice;
3. applyand implement (in a programming language) quantitative models and optimization procedures to solve companies’ revenue optimization problems based on data;
4. Identity opportunities for revenue optimization of companies from various industries.

After completing the course, students should be able to identify and exploit opportunities for revenue optimization in different business contexts, and use quantitative models to deal with various pricing and capacity allocation problems arising in revenue management systems.
Specifically, having successfully completed this course, a student is able to:

1. describe the main elements of revenue management and pricing systems;

2. understand the key concepts and principles in revenue management and pricing;

3. understand how customer-choice modelling can be used in revenue management and pricing;

4. understand quantity-based and price-based revenue management models;

5. construct the appropriate revenue management model for a given situation in practice;

6. applyand implement quantitative models and optimization procedures to solve companies’ revenue optimization problems based on data;

7. Identity opportunities for revenue optimization of companies from various industries

You must meet the following requirements

• Registered for a degree programme other than
• HBO-TOP Applied Physics, Pre-Master
• Completed none of the course modules listed below
• Int & strat risk mgt (1CK80)

• Pricing and Revenue Optimization. 2nd edition. Stanford University Press. (ISBN 9781503610002)
• The Theory and Practice of Revenue Management (Vol. 1). (ISBN 9780387243764)
• Lecture notes written by responsible lecturer based on Talluri & Van Ryzin (2004) (TVR). These notes will provide a deeper and more mathematical treatment of the concepts of revenue management and pricing analytics.