The Evolution of Yield Management in the Airline Industry : Origins to the Last Frontier

This book chronicles airline revenue management from its early origins to the last frontier. Since its inception revenue management has now become an integral part of the airline business process for competitive advantage. The field has progressed from inventory control of the base fare, to managing bundles of base fare and air ancillaries, to the precise inventory control at the individual seat level. The author provides an end-to-end view of pricing and revenue management in the airline industry covering airline pricing, advances in revenue management, availability, and air shopping, offer management and product distribution, agency revenue management, impact of revenue management across airline planning and operations, and emerging technologies is travel. The target audience of this book is practitioners who want to understand the basics and have an end-to-end view of revenue management. Foreword Foreword Foreword The History of Peanuts: The Rise and Rise of Yield Management Foreword Preface Contents 1: Origins 1.1 Introduction 1.2 Origins of the Airline Reservations System 1.3 Airline Deregulation 1.4 Yield Management: The Early Period 1.5 Origins of the Frequent Flyer Programs 1.6 Origins of the GDS 1.6.1 Industry Standards and Governance 1.6.2 Communications Partners 1.6.3 Settlement Partners for Airlines and Agencies 1.6.4 Industry Partners for Airline Fares 1.6.5 Industry Partners for Airline Schedules 1.6.6 GDS and Collaborative Entities 1.6.7 Government Oversight 1.6.8 Airline Divestiture and Deregulation of the GDS 1.6.9 Air Shopping and the GDS 1.6.10 Legacy Technology 1.7 The Growth of the Internet and Online Channels 1.8 The Travel Value Chain 1.9 Revenue Management Storefronts 1.10 Travel Agents: How They Make Money 1.11 Changes in the Distribution Landscape with IATA ́s New Distribution Capability 1.12 The Airline Marketing Planning Process 1.12.1 The Time Frames 1.12.2 Industry Datasets 1.12.3 Scheduling, Pricing, Revenue Management and Distribution Synergies 1.13 Pricing and Yield Management for Competitive Advantage 1.14 Yield Management: The Onward Journey 2: Airline Pricing 2.1 Overview 2.2 Fare Products 2.3 Fare Dimensions and Fare Types 2.4 Booking Class, Fare Category and Fare Basis Code 2.4.1 Fare Classes and Booking Classes 2.5 Classification of Fare Products 2.5.1 Public Fares 2.5.2 Private Fares 2.5.3 Web Fares 2.6 Fare Rule Categories 2.7 Circumventing Fare Rules 2.7.1 Overlapping Flights 2.7.2 Hidden Cities 2.8 Journeys 2.9 Itinerary Pricing 2.10 IATA Traffic Conference Areas 2.11 Constructed Fares 2.12 Savvy Travelers, Stopovers, Open Jaws and Frequent Flyer Redemptions 2.13 Market Segmentation 2.14 How Many Price Points in a Market? 2.15 The Fare Management Planning Process 2.16 Pricing Strategy and its Impact on Tactical Pricing 2.17 Reactive Pricing Process 2.18 Proactive Pricing Process 2.19 Fare Rationalization in the Price Planning Process 2.20 Multilateral and Bilateral Prorate Agreements 2.20.1 The SPA Lifecycle 2.21 Airline Ancillaries 2.21.1 Branded Fares Record (S-8) 2.22 Total Itinerary Pricing with Ancillaries 3: The Airline Spill Model 3.1 Introduction 3.2 Recapture and Upsell 3.3 Spill Model Metrics 3.4 Spill Model Applications 3.5 The Boeing Spill Model 3.5.1 Logit Approximation to the Normal Distribution 3.6 The Gamma Spill Model 3.6.1 The Passenger Closing Rate 3.7 Calibration of Input Parameters 3.7.1 Coefficient of Variation of Demand 3.7.2 Estimation of Load Factor on Closed Flights 3.8 Estimation of Spill 3.8.1 Estimating Spill for a Group of Flights 3.9 First Class Spill Model 3.9.1 Negative Exponential Distribution 3.9.2 Two-Stage Cox Distribution 4: Revenue Management of the Base Fare 4.1 Introduction 4.2 Definition of Flight Leg, Flight Segment, Service and Market 4.3 Revenue Management Alternatives 4.4 Leg/Segment Revenue Management 4.4.1 Host CRS Data Collection 4.4.2 Demand Forecasting 4.4.2.1 Key Differences from Traditional Forecasting 4.4.2.2 Leg Class and Segment Class Demand Forecasting 4.4.2.3 Booking Profiles 4.4.2.4 Standard Profiles 4.4.2.5 Hierarchical Profiles 4.4.2.6 Reservations Holding Cancellation Rate Profile 4.4.2.7 Net Demand Profiles 4.4.2.8 Booked Cancellation Rate Profiles 4.4.2.9 Untruncating Traffic (Censored) Data 4.4.2.10 Demand Untruncation Example 4.4.2.11 Expected Maximization Method 4.4.2.12 Demand Forecast Models 4.4.2.13 Time Series Forecasting 4.4.2.13.1 Simple Moving Average 4.4.2.13.2 Exponential Smoothing 4.4.2.13.3 Constant, Trend and Seasonality Models 4.4.2.13.4 Kalman Filter 4.4.2.13.5 ARMA and ARIMA Models 4.4.2.14 Regression Forecasting 4.4.2.15 Booking Profile Forecasts 4.4.2.16 Combined Forecasting 4.4.2.17 Alternative Approaches to Demand Unconstraining and Forecasting 4.4.2.18 OandD Demand Forecasting: The First Generation 4.4.2.19 OandD Demand Forecasting: The Second Generation 4.4.2.20 Limitations of Single Booking Class Models 4.4.3 Competitive Air Shopping Data 4.4.3.1 OandD Demand Forecasting: The Third Generation 4.4.3.2 Stated Preferences Versus Revealed Preferences 4.4.3.3 Consumer Preference Modeling Approach to Demand Forecasting 4.4.3.4 Multinomial Probit and Nested Logit Models 4.4.3.5 Forecasting All OandD ́s Versus A Must Forecast List 4.4.4 Overbooking 4.4.4.1 Operational Metrics 4.4.4.2 Types of Overbooking Models 4.4.4.3 Forecasting Boarding Rate 4.4.4.4 Oversale Rate Constraint 4.4.4.5 The Binomial Model 4.4.4.6 Normal Distribution of Show Up Process 4.4.4.7 Economic Overbooking Model 4.4.4.8 Calculating Predeparture Overbooking Levels 4.4.4.9 Static Models and Dynamic Models 4.4.4.10 Benefits of Overbooking 4.4.5 Discount Allocation Controls 4.4.5.1 Combined Overbooking and Discount Allocations 4.4.6 Reservations Inventory Controls by Leg/Segment 4.4.6.1 Calculating Seat Availability 4.4.6.2 Segment Close Indicators and Segment Limits 4.4.6.3 Point of Sale Controls 4.4.6.4 Shared Cabin Inventory 4.4.6.5 Funnel Flights/Overlap Flights and Inventory Control 4.4.7 Performance Measurement 4.4.7.1 Standard Performance Metrics 4.4.7.2 Revenue Opportunity Model 4.4.8 Critical Situation Identification 4.5 Origin and Destination (OandD) Revenue Management 4.5.1 First, Second and Third Order Network Effects 4.5.2 Virtual Nesting 4.5.2.1 Dual Indexing 4.5.2.2 Dynamic Virtual Nesting 4.5.2.3 Virtual Nesting Indexing 4.5.2.4 Utilization of Buckets 4.5.2.5 Fares Versus Cumulative Effective Revenue 4.5.3 Continuous Nesting (Bid Price Controls) 4.5.4 Network Optimization Models 4.5.5 Calculation of Seat Availability 4.5.6 Fare Qualification Rules in Passenger Valuation 4.5.7 Alternatives for Creation of Market Values 4.5.8 Post Process Nested Inventory Controls 4.6 Inventory and Legacy Systems 4.7 Industry Impact of OandD Revenue Management 4.8 Branded Fare Products 4.8.1 Branded Fare Family Example 4.8.2 Fare Family Attributes 4.8.3 Challenges 4.9 Connectivity 4.9.1 AVS/AVN 4.9.2 Basic Booking Record (BBR) 4.9.3 Direct Access Interactive (DAI) 4.9.4 Seamless Sell and Seamless Availability 4.9.4.1 Direct Connect Sell (DCS) 4.9.4.2 Direct Connect Availability (DCA) 4.9.5 Market Restricted Flights 4.9.6 Married Segments 4.9.7 Journey Data 4.9.8 Married to Journey 4.9.9 Interactive Seat Maps 4.9.10 Interactive Pre-reserved Seats 4.9.11 Point of Sale 4.9.12 Point of Commencement 4.10 Regaining Control of Off-tariff Fares with OandD Controls 4.11 Availability versus Inventory 4.12 Maintaining Integrity of OandD in Inventory 4.12.1 Codeshare Availability 4.12.2 Out of Sequence Bookings 4.12.3 Integrity of OandD Controls and Mixed Classes 4.12.3.1 Leg/Segment Carriers: Why Do Mixed Classes Occur on the Ticketed PNR? 4.12.3.2 Implications for Leg/Segment Controls 4.12.3.3 OandD Revenue Management Carriers: Why Do Mixed Classes Occur on the Ticketed PNR? 4.12.3.4 Implications for OandD Carriers 4.12.3.5 Thru Fare Precedence 4.13 Significance of Seat Availability for Online and Offline Distribution Channels 4.13.1 Approaches to Determining Availability 4.13.2 Impact of Cached Availability on the Revenue Management Value Proposition 4.13.3 Proxy Based Availability as an Alternative to Cached Availability 4.13.4 Distributed Availability 4.14 Alliances and Partnerships 4.14.1 Origins of Codeshare 4.14.2 Origins of Global Alliances 4.14.3 The Modern Alliances 4.14.4 Codeshare Flights 4.14.4.1 Types of Codeshare 4.14.4.2 Codeshare Availability for Free Sale 4.15 Alliance Revenue Management 4.16 What Revenue Management Capability Does My Airline Need? 4.16.1 Phased Adoption 4.17 Revenue Management for Groups 4.17.1 Types of Groups 4.17.2 Group Evaluation 4.17.3 Allotment Planning 4.17.4 Group Demand Forecasting 4.17.5 Group Attrition Estimation 4.17.6 Group Performance Measurement 4.18 Role of Revenue Integrity 4.19 Impact of Revenue Management in Travel and Other Industries 5: Low-Cost Carriers and Impacts on Revenue Management 5.1 Introduction 5.2 Value Pricing 5.3 Low-Cost Carrier Dynamics 5.4 Inventory Control with Restriction Free Fares 5.5 Coexistence of Inventory Controls for Network Carriers 5.6 Impact of LCC Pricing on Revenue Management 5.6.1 Multi-class and Multi-class Multi-flight models 5.6.2 Impact on Revenue Management Models: Demand Forecasting 5.6.3 Impact on Revenue Management Models: Optimization 6: Offer Management 6.1 Origins of Merchandising 6.2 Offer Management 6.3 An Omni-Channel Strategy 6.4 The Stages of Travel 6.4.1 Customer Segmentation 6.4.1.1 Frequent Flyer Segmentation and Customer Lifetime Value 6.4.2 Personas for Offer Creation 6.4.3 Personalizing the Best Fare Based on Trade-off Analytics 6.4.4 Types of Recommendation Engines 6.4.5 Recommendation Engine for Bundles 6.4.6 Offer Engine 6.4.7 Displaying Offers on the Consumer Direct Channel 6.4.8 Test and Learn Experimentation 6.5 Dynamic Pricing of Offers and the Role of the GDS 6.6 Corporate Travel and Offer Management 6.7 Attribute-Based Room Pricing for Hotels 6.8 Extensions to Non-Air with Stopovers 6.9 Offer Management and Value Scoring for GDS Displays 6.10 Limitations of Supplier and GDS Influenced Offers 6.11 The Universal Profile 6.12 The Universal Data Exchange 6.13 Altering the Customer Value Chain 7: Competitive Revenue Management 7.1 Introduction 7.2 Leveraging Competitive Shopping Data 7.3 Dynamic Availability 7.3.1 Pros and Cons of Dynamic Availability 7.4 Dynamic Pricing 7.4.1 Pros and Cons of Dynamic Pricing 7.4.2 Bridging the Chasm Between the Market Value and Ticketed Fare 8: Agency Revenue Management 8.1 Overview 8.2 Aspects of Agency Revenue Management 8.2.1 Front End Commissions 8.2.2 Back End (Override) Commissions 8.2.3 Net Fare Markup 8.2.4 Bulk Fares and Packages 8.2.5 Optimizing Screen Real Estate 8.2.6 Hotel Product Normalization 8.2.7 Collaboration with Corporations to Optimize Travel Spend 8.3 Summary 9: The Last Frontier: Individual Seat Pricing 9.1 Individual Seat Inventory Control 9.1.1 Seat Map Cache for GDS Shopping 9.1.2 Seat Map Cache for the Direct Channel 9.1.3 Seat-Led Shopping: Agency and Direct Channels 9.1.4 Pricing of Seats 9.1.5 Impact of NDC on Revenue Management 9.2 Milestones in Airline Revenue Management 10: Influence of Revenue Management on the Airline Business Process 10.1 Impact of Revenue Management on the Airline Business 10.1.1 Reservations and Inventory Control 10.1.2 Network Planning and Flight Scheduling 10.1.3 Close-in Re-fleeting 10.1.4 Fare Management 10.1.5 Air Shopping 10.1.6 Loyalty and Coalition Programs 10.1.7 Screen Display Optimization 10.1.8 Offer Management 10.1.9 Pricing of Air Ancillaries 10.1.10 Inflight Catering 10.1.11 Interactive Marketing 10.1.12 Airline Operations 10.2 Coping with the COVID-19 Pandemic 10.2.1 Flight Scheduling 10.2.2 Airline Pricing and Cash Flow 10.2.3 Robust Revenue Management 11: Artificial Intelligence and Emerging Technologies in Travel 11.1 Introduction 11.2 Travel Complexity and AI 11.2.1 Growth in Air Shopping Volumes 11.2.2 Growth in Air Traffic Volumes 11.2.3 Content Fragmentation 11.2.4 IATA New Distribution Capability 11.2.5 Dynamic Pricing 11.2.6 Payment Systems 11.3 Approach for Adoption of AI in Travel 11.3.1 Robotic Process Automation 11.3.2 Cognitive Insight 11.3.3 Cognitive Engagement 11.4 Operations Research at the Crossroads 11.5 Role of AI in Travel 11.5.1 Passenger Name Recognition 11.5.2 Customer Segmentation 11.5.3 Test and Learn Experimentation 11.5.4 Fare Prediction 11.5.5 User Interfaces and Experiential Learning 11.6 Challenge of Interpretability 11.7 COVID-19 and AI 11.8 Quantum Computing and AI 11.9 Building an Organization 11.9.1 Identifying Opportunities for AI 11.9.2 How to Scale 11.10 The Future of AI 11.11 Role of Big Data 11.11.1 Demand Forecasting Based on Consumer Preferences 11.11.2 Hotel Shopping and Dynamic Ranking 11.11.3 Optimizing Air Screen Display 11.11.4 Dynamic Intervention 11.11.5 Hotels Dynamic Pricing 11.11.6 Hotel Competitive Sets 11.11.7 The Chatter Index 11.12 Shopping Query Data 11.13 Blockchain in Travel 11.13.1 Loyalty Programs 11.13.2 Interline Ticketing 11.13.3 Airline/Agency Contracts 11.13.4 Revenue Management 11.13.5 Known Traveler Digital Identity 11.14 The Role of Machine Learning with Blockchain 11.14.1 Maturity of Blockchain in Travel 12: Future State 12.1 Future of Travel 12.2 Core Airline Revenue Management 12.3 Future of the GDS 12.4 E-Commerce Giants and Travel 12.5 Seamless Customer Experience for Travel 12.6 Beyond Travel for a Seamless E-commerce Experience 12.7 Administration of Key Horizontal Enablers by a Neutral Entity Appendix A: Traffic Freedoms First Freedom Second Freedom Third Freedom Fourth Freedom Fifth Freedom Sixth Freedom Seventh Freedom Eighth Freedom Ninth Freedom Appendix B: Airline Industry Acronyms Appendix C: Glossary References Index