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Revenue Management and Strategic Pricing for Service Enterprises. Center for Service Enterprise Engineering The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering Pennsylvania State University. Revenue Management and Pricing. Introduction

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revenue management and strategic pricing for service enterprises

Revenue Management and Strategic Pricing for Service Enterprises

Center for Service Enterprise Engineering

The Harold and Inge Marcus Department


Industrial and Manufacturing Engineering

Pennsylvania State University

revenue management and pricing
Revenue Management and Pricing


Examples and Challenges

Our Research and Proposal




what is revenue management
What is Revenue Management?
  • Revenue Management:
    • “The science of maximizing profits through market demand forecasting and the mathematical optimization of pricing and inventory” (Boyd, 2002)
    • “The use of data, mathematics, and computers to better understand purchasing behavior and to recommend better prices.” (INFORMS)
revenue management and pricing1
Revenue Management and Pricing
  • Revenue Management
    • Originally called yield management in the airline industry, was devised to exploit the power of differential seat pricing
    • Philosophy: a service firm should extract all willingness to pay from customers through differential pricing and product differentiation
the power of differential pricing
The Power of Differential Pricing

Pioneer application to American Airlines contributed $1.4 billion in revenue over a 3 year period with associated profits of $0.892 billion (for 1989-1991)



the power of differential pricing1
The Power of Differential Pricing

Customers are statistically distinguished from one another according to their propensity/ability to pay

Pricing is by customer class or even by individual as well as by service class

Pricing is dynamic

Computer-automated and applicable to large transaction volumes

The new frontier: strategic pricing, wherein the price response of competitors is explicitly considered



industry popularity
Industry Popularity
  • “Now we can be a lot smarter. Revenue management is all of our profit, and more.”Bill Brunger, Vice President Continental Airlines
  • “Revenue Pricing Optimization represent the next wave of software as companies seek to leverage their ERP and CRM solutions.”Scott Phillips, Merrill Lynch
  • “One of the most exciting inevitabilities ahead is ‘yield management.’ ”Bob Austrian, Bank of America Securities
  • “Revenue Optimization will become a competitive strategy in nearly all industries.”AMR Research



application areas
Application Areas
  • RM has been applied and challenged in many different service areas
  • Most active area
    • Airline / Hotel / Car Rental / Rail
  • Recently highlighted area
    • Apparel / Restaurant / Cruise /Cargo
    • Broadcast
    • Healthcare
    • Manufacturing
how to determine price and allocate
How to determine price and allocate?
  • State College -> JFK (250 miles)
  • JFK -> Las Vegas (2570 miles)
fare mix product
Fare mix product




  • M class can be open to customers but B class cannot be.
  • How to handle the surplus for B class seats from SCE to PHL?
  • How to allocate seats for each class?

Y Class


Y Class


available Class

M Class


M Class


B Class


B Class


Q Class


Q Class


Might be loss







hotel reservation
Hotel Reservation
  • Last Minute deal (one week before)
    • Hotel may want to sell out rooms
hotels reservation
Hotels Reservation
  • Hotel (Netessine and Shumsky, 2002)
    • Determine protection level (booking limit)
    • Valuation by decision tree and maximizing revenue
car rental
Car Rental
  • Price changes (Anderson et al. 2004)
large scale
Large Scale
  • More flights and different capacities

Source from

demand forecast
Demand Forecast
  • Pricing under uncertainty
our research and proposal
Our Research and Proposal

Following this slide are slides that give an overview of our relevant research and proposed effort

revenue management and pricing2
Revenue Management and Pricing
  • Service packages
    • Revenue maximization
    • Strategic pricing
    • Demand Management
  • Features
    • Uncertainty
    • Competition
revenue management and pricing3
Revenue Management and Pricing
  • Our Strength:
    • Computability
    • Dynamic Game: Variational method
    • Rules of thumb  Real time decision support
  • Related Field
    • Dynamic Optimization and Game
    • Control Theory
    • Mathematical Programming
    • Simulation
    • Risk Management
research goals impacts
Research Goals, Impacts
  • Comprehensive models, computational techniques
  • Real time data, realistic assumption
  • Application
  • General approach, whole service engineering community
  • Managerial insights, revenue management, randomness, competition
research background revenue management
Research Background: Revenue Management
  • Review
    • McGill and van Ryzin 1999 Transportation Science
    • Bitran and Caldentey 2003 Manufacuting & Service Operations Mangament
    • Boyd and Bilegan 2003 Management Science
    • Elmaghraby and Keskinocak 2003 Management Science
    • Chiang, Chen and Xu 2007 International Journal of Revenue Management
  • Book
    • Talluri and van Ryzin 2004 The Theory and Practice of Revenue Managment
    • Phillips 2005 Pricing and Revenue Optimization
revenue management
Revenue Management
  • Pricing
  • Auctions
  • Capacity control (inventory)
  • Overbooking
  • Forecasting
  • Economics
  • Customer behavior and perception
  • Techniques
  • Competition and alliance
classical example newsvendor
Classical example: newsvendor
  • Profit maximization, stock decision, uncertain demand
    • Porteus 1990
    • Petruzzi and Dada 1999
case characteristics of differentiated security products
Case: Characteristics of Differentiated Security Products

Different types of products

Basic Burglary (4), Value (2), Cellular (2), Expanded (2)

Burglary, Fire, Video surveillance, Integrated system, Access Control

Service-dependent demand

Price-sensitive product demand

Demand-dependent quality (“congestion” at the monitoring center)


Customer Differentiation

Basic-Service Customer

Price-sensitive customer

Value-Service Customer

Service Preference

Basic service

Full package

Upgraded service

Period 1

Period t

Period T



needs game based dynamic pricing
Needs Game-Based Dynamic Pricing

Strategic Pricing: The Need to Set Prices Considering Competitors’ Responses

Maximize profit in light of competitors’ strategic actions

Develop multi-year plan to displace rivals

Company #1.

Company #3

Company #2



our proposal
Our Proposal

We propose to develop software for dynamic and strategic pricing for security service companies.

In the event service companies have already invested in revenue management, we can add a strategic pricing capability not available elsewhere.

Following this slide are several slides that give an overview of our proposed effort.

flows for model development
Flows for Model Development





  • Preparation
    • Establish business hypotheses to derive customer/product/ market attributes
    • Prepare for data and inspect information
  • Identification
    • Identify attributes based on given information and determine main factors
  • Optimization
    • Develop a base model for differential pricing and product differentiation
    • Extend the model to a strategic setting, anticipating price response of competitors
  • Validation
    • Inspect pricing strategies and recommendation
    • Validate the revenue management model in a sample market
    • Update the model based on results
    • Implementation

Dynamic Optimization



Analysis, and Implementation

Identify Attributes

Determine Factors

Business Analysis

Data Inspection

special features of our modeling activities
Special Features of Our Modeling Activities
  • We will work off-site with synthetic data to protect SEE center’s clients
special features of our modeling activities1
Special Features of Our Modeling Activities
  • Dynamic (daily, monthly, quarterly and yearly) pricing
  • Exploration of event based pricing, penalties and discounts
special features of our modeling activities2
Special Features of Our Modeling Activities
  • Demand learning: modeling will use moving averages and statistical filtering to constantly and automatically improve estimates of service demands needed to do effective pricing
  • Strategic Pricing: modeling of competitors and their likely price responses to changes in SEE center client’s pricing
  • The next several slides provide some details of our relevant research
literature review
Literature Review
  • Dynamic Optimization and Game
    • Friesz et al. (2005) consider joint pricing and resource allocation in network revenue management markdown optimization with known demand dynamics and parameters.
    • Fixed point algorithm.
  • Kalman Filter
    • Kwon, Friesz, Mookherjee, Yao and Feng(2006) present discrete Kalman-Filter model to forecast the demand and a differential variational inequality model for pricing the service. We also propose an algorithm based on a gap function to efficient computing the optimal pricing strategies.

Perfect information (imperfect information)

Demand is deterministic (uncertainty via learning, robust optimization, data driven)

Single product (network)

Single resource (network)

Single period (multiple period, continuous time)

Sellers optimization, pricing (resource allocation)

Single seller (game)

Complete market, risk neutral (incomplete market, risk averse)




How should sellers price the product and allocate resource with competition?

What are the equilibrium prices in the market?

How to handle demand uncertainty?

How to handle demand learning?

How to handle risk preference?



forecasting and optimization
Forecasting and Optimization

Demand uncertainty

Demand distribution and parameters

Revealed over time

High frequency of data

Information technology

Rich information

Simultaneously forecast the demand and optimize the pricing strategy.



sellers decentralized problem
Sellers’ Decentralized Problem

Sellers Maximize profit over whole time horizon

Decisions Prices

Allocation of capacity

Constraint Demand dynamics

Learning dynamics

Bounds on price

Bounds on capacity

Bounds on demand

proposed research
Proposed Research
  • Our objective in this research is to develop pricing models which simultaneously forecast demand and optimize the pricing strategy under competition and uncertainty.
  • More specifically, we propose a continuous time estimation of parameters using Kalman Filter and markdown dynamic pricing optimization model.
current status
Current Status
  • We present a differential variational inequality model and an algorithm based on a gap function.
  • We have described the dynamics of demand as a continuous time differential equation based on an evolutionary game theory perspective.
  • Realized sales data are refined on a discrete time scale and used to obtain estimates of parameters that govern the evolution of demand.
looking forward
Looking Forward
  • Combine markdown optimization with continuous time parameter estimation.
    • Develop and experiment nonlinear estimation for continuous time system with discrete measure by extending Kalman Filter method for complex revenue management models using insights gained from the discrete time of the model.
  • Service Network
    • Service lever guarantee, product differentiation
    • Future contract market and spot market
    • Both standard analytical approach and computational (numerical) approach
  • The next several slides provide the deliverables of our services
levels of services
Levels of Services



level of services
Level of Services
  • Level 1
    • Report core attributes which will affect revenue
    • Extract customer, product, market attributes from survey, literature, and historical market data
    • Comparison of services with competitors
    • For example, customers’ deviation from product A to B, total market demand, customer’s price sensitivity, costs for each service



level of services1
Level of Services
  • Level 2
    • Report will include a mathematical revenue management model
    • Test the performance when our model is applied in the simulation
    • For small business, apply strategic policies given by our proposed model and report the performance
    • Derive other core attributes in the real system
  • Level 3
    • Report will include an advanced revenue management model
    • Revised model extends to a large scale system
    • Develop a state of art software and train use and interpretation



deliverables and guarantee
Deliverables and Guarantee
  • Cutting Edge game-theory-based dynamic pricing will be instantiated as software for service companies
  • Fresh “highly technical” PhD students will perform the necessary research and development using synthetic data
  • Final delivery of the product to SEE center clients’ computers, including the incorporation of secure data, will be made by professors who have signed non-disclosure agreements
  • Or, the Center can host the model on its secure computers
  • Guarantee: we will re-engineer any aspect of the model that does not meet with client’s approval