Dr. Zhan Pang (庞湛)
Lecturer in Operations Research
Office Hours: Monday 2PM-4PM or by appointment in Room A59
MSCI583 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 course aims to introduce this multi-discipline subject to the master students across different programmes related to Management Science and those who plan to start their careers as business analysts.
Business Analytics has been recognized as a new source of competitive advantage, more and more organizations. 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 optimize business processes to achieve business objectives. It synthesizes statistics and data science, mathematical and behavioural science, and business principles.
This pricing course 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 course 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) andsupply-side management (e.g., capacity allocation and inventory control) in competitive market environments. Starting from Airline industry in 1970’s, it has grown into a mainstream business practice in varieties of service industries (e.g., Walt Disney Land, 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 optimization techniques to help service providers to maximize their revenue for perishable assets.
Pre-requisites: Introductory statistics, optimisation, SPSS, and SAS.
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
Software: MS Word/Excel, IBM SPSS, SAS 9.2
1. 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?
2. Descriptive Pricing Analytics (workshop 1)
- How to use data to reflect what happened?
3. Predictive Pricing Analytics (workshop 2)
- 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?
4. Prescriptive Pricing Analytics (workshop 3)
- How to model pricing decision-making processes?
- How to optimize pricing decisions?
5. Experimental Pricing Research (workshop 4)
- How to learn consumer preference in experiments?
- How to design new products and their pricing strategies?
6. Revenue Management
7. Guest Lecture (TBD)
- How to manage perishable resources with multiple market segments?
- How service providers such as airlines, hotels, etc. maximize their revenue?
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.
- Bodea, T., Ferguson, M. E. (2012) Pricing: Segmentation and Analytics. Business Expert Press.
- Raju, J., J. Zhang. (2010) Smart Pricing.
- 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.
- Professional social media: Linkedin’s Pricing Related Groups. You can also find the job description of the pricing analyst by searching “pricing” from the job category
Industry Partner: PROS
Other useful links:
Deloitte: Pricing Analytics: The three-minute guide
Nielson: Pricing Analytics
Symphony Teleca: Pricing Analytics
PROS: Pricing Solutions
SAS® Revenue Management & Price Optimization Analytics
Columbia Business School: Pricing Analytics (Executive Program)