Overview of PODS Consortium Research

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1 Overview of PODS Consortium Research Dr. Peter P. Belobaba MIT International Center for Air Transportation Presentation to ATPCO Dynamic Pricing Working Group Washington, DC February 23, 2016

2 MIT PODS Research Consortium 13 international member companies in 2016: Lufthansa/Swiss LATAM Airlines Delta Air Lines Amadeus Qatar Airways Emirates Airline Air Canada United Airlines Boeing Commercial Airplanes PROS American Airlines Etihad Airways ATPCO Airline revenue management research at MIT funded by PODS Research Consortium Passenger Origin-Destination Simulator of passenger choice and airline RM actions in competitive networks Findings used to help guide RM system development 2

3 PODS Research Topics RM control of multiple classes in premium cabin, and joint dual cabin optimization issues Point of Sale RM controls impacts of abuse on revenue gains Modeling passenger cancellation behavior and impacts of cancellation forecasting methods on RM systems New forecasting and optimization methods for fare family bundled product structures Accounting for ancillary revenue potential in RM optimization and availability Potential for dynamic availability/fare quote adjustments, looking toward personalized RM Methods for dynamic pricing without booking classes 3

4 Most Recent Thesis Titles Fry, D., Demand Driven Dispatch and Revenue Management, May 2015 Hao, E., Ancillary Revenues in the Airline Industry: Impacts on RM and Distribution Systems, May 2014 Lepage, P-O., Performance of Multiple Cabin Optimization Methods in Airline Revenue Management, MST/OR Dual Degree Thesis, May 2013 Surges, V., RM Methods for Airline Fare Family Structures, SM Aero/Astro Thesis, May Abramovich, M., Impacts of Revenue Management on Estimates of Spilled Passenger Demand, SM CDO, May

5 Passenger Origin-Destination Simulator(PODS): Competitive Environment with Passenger Choice Airline Which path/classes should I make available? Which of the available path/classes do I prefer? Passenger 5

6 Passenger Choice Process Demand generation Total demand for air travel per O-D market per passenger type per departure date Passenger characteristics Passenger type, Decision window, WTP, disutilities Passenger choice set Available travel alternatives Decision rule Advance purchase requirements Affordable travel alternatives (Fare < WTP) Path/class open/closed status Choose alternative (path/class) that has the lowest generalized cost (Fare + disutilities) RM Optimizer Demand Generation Passenger characteristics Passenger choice set Passenger decision Decision Window Model 6

7 Summary: Passenger Decision Model 100% 80% 60% 40% 20% 0% DEMAND GENERATION Percent booked leisure Days to departure 40 business Passenger arrival 0 PASSENGER CHARACTERISTICS (by passenger type leisure or business) Probability of paying 100% 80% 60% 40% 20% Willingness to pay 0% $50 $100 Price $150 $200 Disutility costs Saturday night Non-refundable Change fee AVAILABILITY Itinerary Itinerary 1 Itinerary 2 Itinerary 3 Fare class Y B M Q Pricing/Revenue Management GENERALIZED COST OF EACH FEASIBLE ALTERNATIVE $0 $100 $200 $300 PASSENGER CHOICE Time window Courtesy T. Gorin 7

8 International Network U10 Airline 1 MSP Network(EMSRb) Airline 2 ORD Network(DAVN) Airline 3 MCI Domestic Network(AT90) Airline 4 DFW Network(DAVN) 8

9 Three Fare Product Sets Domestic Restricted FC AP MIN3 CXL SAT Domestic Less Restricted International Restricted FC AP MIN3 CXL SAT FC AP SAT CXL MAX

10 RM Algorithms for Fare Families Vinnie Surges Peter Belobaba

11 Background on Fare Families Fare Families are an innovative approach to airline pricing and segmentation Several (branded) product families differentiated with clearly defined restrictions and/or amenities Multiple price points within each product family Can be attractive to both airlines and passengers Airline revenues and load factors increase Lower fare leisure passengers can book closer to departure Early-booking business travelers can book cheaper tickets in business family Numerous RM challenges associated with fare families Traditional RM systems not designed for multiple families of booking classes 11

12 Fare Families: Air New Zealand 12

13 Two identical fare families: Family 1 Unrestricted Family 2 Fully Restricted Five price points in each family Fare Family Structure with Non-Overlapping Price Points 13

14 Passenger Decision Process in a Fare Family Structure Appropriate algorithms for fare families must account for passenger WTP, as well as product preference (between the lowest open class in each family) f1 = lowest open family 1 class f2 = lowest open family 2 class 14

15 Fare Family Availability Policies Policy: Pairs of fare classes an airline can make available Only consider policies of lowest open pair of family 1 and family 2 classes (f1,f2) With 5 classes in each family, this amounts to (5)(5+1) = 30 distinct policies Policy Lowest open Family 1 Lowest open Family 2 1 A1 % 2 A1 A2 3 A1 B2 6 A1 E2 7 B1 % 8 B1 A2 12 B1 E2 25 E1 % 26 E1 A2 30 E1 E2 15

16 New Forecasting Approach: QFF3 Each step performed in every time frame or data collection point (DCP) prior to departure 16

17 Network D10: Dual Airline Competitive Simulation 2 airlines; 40 Spoke Cities 252 legs; 482 OD markets 10 fare classes 17

18 Each airline offers two identical fare families: Family 1 - Unrestricted Family 2 - Fully Restricted Five price points in each family Non-Overlapping Fare Family Structure AP requirements only used in base case with Standard Forecasting No AP used with any fare family forecasting method (QFF) 18

19 Baseline Revenues and Fare Class Mix Base Case: AL1: EMSRb with Standard Forecasting (with AP) AL2: EMSRb with Standard Forecasting (with AP) $ AL1 Booking Class Mix Revenues $ $ $ Total Bookings Business Leisure $ Airline 1 Airline 2 Load Factor (%) A1 B1 C1 D1 E1 A2 B2 C2 D2 E2 AP encourages sell-up by both business and leisure travelers

20 Airline 1 with HF or QFF: Revenues for Both Airlines Airline 1 uses HF or QFF while Airline 2 uses Standard Forecasting $ $ Airline 1 Airline % 12.5% 12.7% Revenues $ Standard $ % $ $ HF QFF1 QFF2 QFF3 Airline 1 Forecasting Methods Load Factor (%) Substantial revenue gains achieved with appropriate fare family RM Yield increase outweighs slight decrease in LF 20

21 Fare Family QFF: Conclusions QFF3 Algorithm Incorporates characteristics of first two algorithms Separate sell-up inputs by fare family Buy-across accounted for through buy-up and buy-down Performance in Competitive PODS Environment All methods produced substantial revenue gains relative to Hybrid Forecasting QFF3 resulted in highest revenues in leg-based experiments But, very sensitive to estimates of WTP inputs by airline 21

22 Ancillary Revenue Management and New Distribution Capability (NDC) Eric Hao Peter Belobaba

23 How to Account for Ancillary Revenues in Availability? If some fare classes are more likely to generate ancillary revenues, should availability be higher? A low-fare booking worth $125 in ticket fare might have an expected incremental ancillary revenue of, say, $30 Economic expectation some $125 bookings buy no add-ons, others buy $60 of add-ons, but expected value is $30 Two alternatives for including ancillary revenue potential in availability calculations 1. Include $30 expectation in RM system optimization model 2. Real-time adjustment of availability in inventory system 23

24 Method 1: RM Input Adjustment with Class Level Estimates of A.R. Class Fare Booking Limit 1 $ $ $ $ $ $ SEATS Assume: Lowest four classes expected to pay $30 in add-ons. Class Fare Booking Limit 1 $ $ $ $ $ $

25 Method 1: Incremental Revenue Losses with Standard Forecasting Impacts at Medium Demand (83% LF) 0.00% -0.20% Low Demand Medium Demand High Demand Ticket Rev. Load Factor Total Revenue -0.40% -0.60% -0.80% -1.00% -1.20% Leg EMSRb 1.9% +2.0 pt. 1.1% -1.40% -1.60% -1.80% DAVN 1.0% +0.8 pt. 0.6% Leg EMSRb DAVN Smaller revenue loss with Network RM Network optimizer in DAVN more effective in selecting higher value, local Class 6 passengers. 25

26 Standard Forecasting Passenger Gain/Loss by Class Percentage of Bookings 20% 15% 10% 5% 0% -5% -10% -15% % Class 1 Class 2 Class 3 Class 4 Class 5 Class 6 Losses from more buy down due to more availability. 1. Benefit: Higher valuation of lower class passengers resulted in more seats and therefore more revenue (ticket and ancillary) from Class Cost: Extra seat availability for lower classes allowed passengers from upper classes to buy down. 26

27 Positive Results with Hybrid Forecasts and Marginal Revenue Optimization Incremental Revenue Gains of RM Input Adjustment for Ancillary Fees Impacts at Medium Demand (83% LF) 0.80% 0.60% 0.40% 0.20% 0.00% -0.20% -0.40% -0.60% -0.80% Low Demand Medium Demand High Demand Hybrid Forecasting and Fare Adjustment Standard Forecasting Revenue Gains Standard Forecast Ticket Ancillary Total 1.0% +2.2% 0.6% HF/FA +0.1% +4.1% +0.6% With WTP forecasting, RM Input Adjustment does well Slightly higher ticket revenue coupled with increased ancillary revenues 27

28 Method 2: Availability Adjustment Seat availability determined by booking limits and bid prices from RM system Adjustment of request value in real-time can increase seat availability to account for Leg Virtual Buckets 1 Actual or estimated ancillary revenue increment (e.g., $30) 2 Assume each Class 6 booking generates $30 in added revenue 3 Increased availability for Class 6, which increases ancillary revenues BID PRICE $

29 Availability Adjustment in DAVN with Standard Path Forecasting Impacts at Medium Demand (83% LF) 1.50% 1.00% Load Yield Ticket Revenue Ancillary Revenue 0.50% 0.00% RM Input +0.8 pt. -1.9% -1.0% +2.2% -0.50% -1.00% -1.50% Low Demand Medium Demand High Demand Availability +6.0 pt. -7.7% -1.2% +8.4% RM Input Adjustment Availability Fare Adjustment Greater increase in availability than RM input adjustment With the extra bookings, more ancillary revenue gained. But loss in ticket revenue causes net revenue loss, except at low demand 29

30 Both Methods Increase Total Revenues with Hybrid Forecasts and MRO 1.00% 0.90% 0.80% 0.70% 0.60% 0.50% 0.40% 0.30% 0.20% 0.10% 0.00% Incremental Revenue in DAVN with HF/FA Low Demand Medium Demand High Demand Load Yield Ticket Rev. Ancillary Rev. RM Input +2.3 pt. -2.5% +0.1% +4.1% Availability +4.2 pt. -4.7% +0.0% +6.2% RM Input Adjustment Availability Adjustment With advanced RM, extra availability is beneficial for ancillary revenue while maintaining ticket revenues Total revenue gains of 0.5% to 0.8% at medium demand 30

31 Looking to the Future: Customized Availability for Ancillary Spend Passengers have varying ancillary revenue potential A typical fare class 6 passenger might spend an average of $30 But, many might spend zero, while a few might spend $90 What if airlines could give only individuals with higher ancillary revenue potential better availability? Given perfect knowledge of each individual s actual (or expected) ancillary revenue spend Not feasible (yet), but simulations provide an indication of possible revenue gains New Distribution Capability (NDC) will provide airlines with a mechanism for making customized offers 31

32 3.00% Incremental Revenue Gains with Custom Availability Adjustment 2.50% 2.00% 2.49% 2.64% 2.15% 1.50% 1.00% 0.50% 0.00% Low Demand Medium Demand High Demand Availability Adjustment RM Input Adjustment Custom Av. Adj. (k=0) Custom Av. Adj. (k=0.3) Custom Av. Adj. (k=0.6) Custom Av. Adj. (k=0.9) With customized availability, revenue gains increase with variability of individual customer ancillary spending At k=0.9, revenue gains range from 2.1% - 2.6%. 32

33 Challenges: Ancillary Revenues Revenue Management Systems Simple adjustments to existing optimizers are not adequate WTP forecasting and advanced optimization required to effectively incorporate ancillary revenue potential Database Requirements RM optimization of ancillary revenues not possible without historical data to feed future forecasts Most airlines do not keep track of ancillary sales by flight, fare class or passenger Distribution System Issues Traditional availability process presents numerous obstacles to effective distribution and control of ancillary sales 33

34 New Research: Classless Dynamic Pricing Initial investigations of classless RM with truly dynamic pricing Passengers are offered a quasi-continuous set of prices instead of a specific fare product at the time of booking Proposal: solve for FARE that matches/exceeds the RM bid price at each time slice in DP optimizer In a fully undifferentiated pricing structure with lowest fare BFARE The arrival probability of a passenger paying FARE is then a function of the sell-up from BFARE to FARE Compared to Q-forecasting with fare adjustment (MRO) for fixed number of price points 34

35 Classless DP for Dynamic Pricing Example: Classless vs. Q/FA bidprices Lowest Available Fare Bidprice Q/FA 5 class Classless Capacity 35

36 Passenger Origin Destination Simulator The MIT PODS Research Consortium thanks the Boeing Company and PODS Research LLC for providing and supporting the PODS simulation. PODS was first developed at Boeing in the 1990s by Hopperstad et al, and has been enhanced in cooperation with the MIT PODS Consortium. The PODS simulation software is owned by 36

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