Introductory description

The module shall provide the conceptual understanding, practical skills and experience in using programming tools for the students to model and analyse demand, and to use the outcomes to optimise pricing or product availability decisions in an automated fashion.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module makes heavy use of case studies and applied programming exercises in R to study the following topics:
Price differentiation, fences, fairness and acceptance.
Estimation of popular demand models.
Unconstraining of sales data.
Conjoint analysis.
Discrete choice model estimation.
Machine learning models in pricing.
Capacity control models (linear programming, dynamic programming, decomposition techniques).
Dynamic pricing: promotions, markdowns.
B2B customized pricing.

Learning outcomes

By the end of the module, students should be able to:

• Understand key concepts including the impact of constrained capacity, opportunity costs, customer response, demand uncertainty.
• Evaluate the critical differences among different types of opportunity and the approaches needed to address them.

Indicative reading list

Bodea, T. and Ferguson, M. Segmentation, Revenue Management, and Pricing Analytics. Routledge 2014.

Subject specific skills

Estimate various demand models in R.
Solve relevant price or capacity control optimisation models in R.
Be able to compare the performance of different decision policies in simulation studies.

Transferable skills

Numeracy and problem solving: model demand and formulate and solve price or capacity control decisions as constrained optimisation problems.