Pricing Analytics and Revenue Management

Pricing is a fundamental business discipline that is closely related to corporate strategy, marketing, finance, and operations across the whole business process.

This module aims to introduce this multi-discipline subject to the students across different programmes related to management science and those who plan to start their careers as business analysts.

Business analytics has been recognised as a new source of competitive advantage, more and more organisations. It embraces data-driven business intelligence and analytics to gain insights. Pricing analytics, as one of the critical territories of business analytics, has emerged as an important weapon to gain profitability and drive high performance. It becomes the core skill of pricing analyst.

We will introduce pricing analytics at three levels: Descriptive pricing analytics (level one) - use data to demonstrate what happened in the past and what is happening now. Predictive pricing analytics (level two) – use statistical modelling to predict potential future outcomes and explain the drivers of the observed phenomena. Prescriptive pricing analytics (level three) - associate decision alternatives with the prediction of future outcomes to optimise business processes to achieve business objectives. It synthesises statistics and data science, mathematical and behavioural science, and business principles.

This pricing module is largely centred in the quantitative skills in pricing analytics. We will also associate the pricing analytics to behaviour science (e.g., consumer behaviour) and social science (e.g., social networking). The module is structured according to the three levels of analytics (descriptive/predictive/prescriptive), connecting theory to practice through the business contexts and data analysis.

During the lectures, we may also discuss other topics on the strategic pricing management, including price competition (price war), online mechanism design (auctioning and bidding), and contract design and negotiation between businesses, and digital marketing and social network marketing.

Revenue management or yield management is a growing business discipline that integrates demand-side management (e.g. segmentation, pricing and availability) and supply-side management (e.g. capacity allocation and inventory control) in competitive market environments. Starting from airline industry in 1970s, it has grown into a mainstream business practice in varieties of service industries (e.g. Walt DisneyLand, hotels, car rentals) and some manufacturing industries (e.g. Ford). It has also created its own supporting industry with established consulting firms, IT solution providers. Major airlines (e.g. AA, BA, Continental, Lufthansa and SAS) have large numbers of staffs of IT and OR analysts working on revenue management. This part aims at introducing the relevant modelling and optimisation techniques to help service providers to maximise their revenue for perishable assets.

Pre-requisites: introductory statistics, optimisation, SPSS, and SAS.

Different from most of other module, this module demands knowledge from many quantitative subjects. For those who are not confident on (or who lack the background of) the quantitative techniques and do not plan to work in this area with the relevant skills in the future, it is better to take other modules.

Learning outcomes

By the end of the module you should be able to:

  • Understand strategic and tactic roles of pricing in relevant business contexts
  • Know how to model real-world pricing decision making processes
  • Provide business insights using data and analytics
  • Know how to implement pricing solutions
  • Know how to measure financial performance of pricing 

Outline lecture plan

Lectures: ten hours (five two-hour sessions)

Workshops: four hours

Software: MS Word/Excel, IBM SPSS, SAS 9.2

Pricing in business and its economics foundation

  • Why is pricing relevant almost everywhere?
  • What is the role of pricing in firms’ profit levers?
  • How to measure the effect of pricing decisions on demand?

Descriptive pricing analytics (workshop one)

  • How to use data to reflect what happened?

Predictive pricing analytics (workshop two)

  • How to use data to predict what will happen?
  • How to model and estimate the structure of price-response demand curves?
  • What does consumer behaviour matter?

Prescriptive pricing analytics (workshop three)

  • How to model pricing decision-making processes?
  • How to optimise pricing decisions?

Experimental pricing research (workshop four)

  • How to learn consumer preference in experiments?
  • How to design new products and their pricing strategies?

Revenue management

How to manage perishable resources with multiple market segments?
How service providers such as airlines, hotels, etc. maximise their revenue?


100% coursework.

Coursework requires integrating the knowledge of the three levels of analytics and provide insights. A typical coursework is to conduct a case study with a sample of pricing related data. You are required to summarise the patterns of the data using summary statistics and the relevant business context (prescriptive analytics). With the price and and sales information, you are required to forecast and estimate the demand structure and provide the insights (predictive analytics). Finally, with the demand functions, you are required to develop the decision models, optimise the pricing decisions, and provide practical insights.


Lent Term.

Core texts

We do not have a single book for this new subject. The following are some useful sources. You can find most of concepts involved in this course.

Phillips, R. (2005) Pricing and Revenue Optimization. Stanford University Press.
A textbook written by a consultant. There are some copies in the library.

Raju, J., J. Zhang. (2010) Smart Pricing.
There are many interesting pricing stories and case studies in this book.

Orme, B.K. (2010) Getting Started with Conjoint Analysis. A brief introduction of conjoint analysis (read chapter one)

Journals: Wall Street Journal, New York Times, Financial Times, HBR, etc.

Module tutor

Dr Zhan Pang