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Revenue Management and Strategic Pricing for Service Enterprises

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  1. 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

  2. Revenue Management and Pricing Introduction Examples and Challenges Our Research and Proposal Deliverable 2014-06-09 2

  3. 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)

  4. 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

  5. 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) 2014-06-09 5

  6. 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 2014-06-09 6

  7. 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 2014-06-09 7

  8. 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

  9. Industry Examples

  10. How to determine price and allocate? • State College -> JFK (250 miles) • JFK -> Las Vegas (2570 miles)

  11. Fare mix product PHL SCE LAS • 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 100 Y Class 70 available Class M Class 50 M Class 15 B Class 20 B Class 0 Q Class 0 Q Class 0 Might be loss

  12. SCE PHL SCE PHL LAS

  13. Hotel Reservation • Last Minute deal (one week before) • Hotel may want to sell out rooms

  14. Hotels Reservation • Hotel (Netessine and Shumsky, 2002) • Determine protection level (booking limit) • Valuation by decision tree and maximizing revenue

  15. Car Rentals

  16. Car Rental • Price changes (Anderson et al. 2004)

  17. Challenges

  18. Large Scale • More flights and different capacities Source from http://www.continental.com/

  19. Demand Forecast • Pricing under uncertainty

  20. Competitions

  21. Our Research and Proposal Following this slide are slides that give an overview of our relevant research and proposed effort

  22. Revenue Management and Pricing • Service packages • Revenue maximization • Strategic pricing • Demand Management • Features • Uncertainty • Competition

  23. 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

  24. 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

  25. 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

  26. Revenue Management • Pricing • Auctions • Capacity control (inventory) • Overbooking • Forecasting • Economics • Customer behavior and perception • Techniques • Competition and alliance

  27. Classical example: newsvendor • Profit maximization, stock decision, uncertain demand • Porteus 1990 • Petruzzi and Dada 1999

  28. 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)

  29. 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 2014-06-09 29

  30. Case: Differentiated Products and Prices for security services

  31. 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 2014-06-09 31

  32. 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.

  33. Flows for Model Development Preparation Optimization Validation Identification • 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 Development Validation, Analysis, and Implementation Identify Attributes Determine Factors Business Analysis Data Inspection

  34. Special Features of Our Modeling Activities • We will work off-site with synthetic data to protect SEE center’s clients

  35. Special Features of Our Modeling Activities • Dynamic (daily, monthly, quarterly and yearly) pricing • Exploration of event based pricing, penalties and discounts

  36. 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

  37. Literature Summary (contains technical jargon)

  38. 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.

  39. Assumptions 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) 2014-06-09 39

  40. Questions 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? 2014-06-09 40

  41. 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. 2014-06-09 41

  42. 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

  43. 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.

  44. 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.

  45. Numerical Example (Competition) • Revenue Changes

  46. 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

  47. Levels of Services 2014-06-09 47

  48. 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 2014-06-09 48

  49. 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 2014-06-09 49

  50. 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