Market/Airline/Class (MAC) Revenue Management RM2003

1. Market/Airline/Class (MAC) Revenue ManagementRM2003 Hopperstad May 03

2. Issues • Model structure • Background: PODS • Functional form • Some results • Potential real-world application • Lines of inquiry

3. Airline RM modeling assumptionsa short (public) history • 80’s – leg/fare class demand independence  6 to 8% revenue gains over no RM • 90’s – path (passenger itinerary)/class demand independence  1 to 2% revenue gains over leg/class RM • Current – excursions into path demand independence  ½% revenue gain over path/class RM

4. Airline RM modeling assumptions • Yet, anyone who has ever taken an air trip knows that flights are picked on a market basis • trading-off airlines, paths, fares and fare class restrictions • Thus, an ultimate RM system must be market-based • However, market-based RM is a giant step • it is proposed here that a small next step is to assume independent market/airline/class demand

5. Background: PODSpassenger origin/destination simulator • PODS is a full-scale simulation in the sense that: • passengers by type (business/leisure) generated by their • max willing-to-pay (WTP) • favorite/unfavorite airlines & the disutility attributed to unfavorite airlines • decision window & the disutility assigned to paths outside their window • disutility assigned to stops/connects • disutility assigned to fare class restrictions • passengers assigned to best (minimum fare + disutilities) available path with a fare meeting their max WTP threshold • RM demand forecasts based on historical bookings

6. Background: PODS • Leg/class baseline: Expected Marginal Seat Revenue (EMSR) • Three path/class RM systems available in the current version of PODS • NetBP • ProBP • DAVN

7. Background: PODS • EMSR processes (virtual) classes on leg in fare class order • solves for the forecast demand and average fare for the aggregate of all higher classes • obtains a protection level of the aggregate against the class • sets the booking limit for the class (and all lower classes) as the remaining capacity – protection level

8. Background: PODS • NetBP solves for leg bidprices (shadow price) using a network flow LP equivalent • path/class is marked as available if the fare is greater than the sum of the bidprices of the associated legs

9. Background: PODS • ProBP solves for leg bidprices by iterative proration • prorate path/class fare by ratio of bidprices of associated legs • for each leg order the prorated fares and solve a leg bidprice using standard (EMSR) methodology and re-prorate • path/class is marked as available if the fare is greater than the sum of the bidprices of the associated legs

10. Background: PODS • DAVN uses the bidprices from NetBP as displacement costs and then for each leg • reduces path/class fare by the displacement from other leg(s) • creates (demand equalized) virtual classes • uses standard (EMSR) leg/class optimizer to set availability

11. Architecture • Embed NetBP/ProBP/DAVN in a MAC shell rather than develop a new optimizer (for now) • Use current PODS forecasters and detruncators • pickup and regression forecasting • pickup, booking curve and projection detruncation • aggregate path/class observations into MAC observations • Assumption: all spill is contained within a MAC until all paths (of index airline) are closed for the class

12. allocate MAC forecasts to associated path/classes solve for leg bidprices re-allocate spill from newly closed path/classes to open path/classes close path/classes with fares less than sum of bidprices for the associated legs* any new path/classes closed? yes no quit *Rule: no path/class can be re-opened Optimizers • Bidprice engine (NetBP, ProBP)

13. allocate original MAC forecasts to associated path/classes and create virtual classes using final MAC bidprices solve for leg/virtual class availability recalculate leg/virtual class demand close path/classes that have been assigned to closed virtual classes on associated legs re-allocate spill from newly closed path/classes to open path/classes yes any new path/classes closed? no quit Optimizers • Path/class availability solver (DAVN)

14. Additional technology • First-choice preference estimation for paths of a MAC • constructed from historical bookings for open paths • iterative procedure to account for partial observations (not all paths open for a class) • Assumption: second-choice, third-choice,…… preference can be calculated as normalized (removing closed paths) first-choice preference

15. Additional technology • Estimation of spill-in rate from, spill-out rate to competitor(s) • Key idea: equilibrium • if the historical fraction of weighted paths open for time frame for the index airline (hfropa) and the competitor(s) (hfropc) is observed • and if the the current fraction of weighted paths open is observed for both the index airline and the competitor(s) (fropa, fropc) • then when fropc is less than hfropc, spill-in must occur • and when fropc is greater than hfropc, spill-out must occur • Fraction of competitor paths open inferred from local path/class availability (AVS messages)

16. Additional technology • Competitor demand estimation • based on observed historical market share(which is also a function of equilibrium) • uses booking curves to adjust for limited (input) time horizon • Spill-in/spill-out defined by adjusted competitor demand and maximum spill-in rate across classes • Assumed that once MAC demand modified for spill to/from competitor, all spill is contained within a MAC

17. HUBAL 1 20 CITIES 20 CITIES HUBAL 2 Some results • PODS network D • 2 airlines • 3 banks each • 252 legs • 482 markets • 2892 paths • 4 fare classes • Demand • demand factor = 1.0 • 50/50 business/leisure

18. Results 1 • Airline 1 uses one of the path/class systems • without a MAC shell • with a MAC shell • Airline 2 uses the PODS standard leg/class system (EMSR) • Results quoted as % revenue gains compared to both airlines using EMSR

19. Results 1 +MAC +MAC +MAC revenue gain NetBP ProBP DAVN

20. Results 2 • Airlines 1 and 2 follow a sequence of RM using DAVN • start with both using EMSR • move 1: airline 1 adopts DAVN • move 2: airline 2 adopts DAVN • move 3: airline 1 adopts DAVN + MAC • move 4: airline 2 adopts DAVN + MAC • Results quoted as % revenue gains compared to both airlines using EMSR

21. Results 2 revenue gain AL1 DAVN AL2 DAVN AL1 MAC AL2 MAC

22. Results 3 • Components of MAC revenue gain • optimizer (NetBP, ProBP, DAVN) by itself • MAC without spill-in/spill-out • MAC spill-in/spill-out • Results quoted as % revenue gains compared airline 1 using EMSR

23. revenue gain NetBP ProBP DAVN Results 3 Note: Mac spill gain dominated by spill-in compared to spill-out

24. Potential real-world application of MAC • Can’t say how difficult • But can propose it will provide for a new level of technical integration of RM and the rest of the airline • use of external path preference models to determine first-choice preference, conditional second, third,…. preference and account for the effect of schedule changes • use of external marketing data, econometric models, etc. to define at least components of market demand

25. Lines of inquiry • New optimizer that integrates the MAC arguments • rather than embedding in a shell • Model vertical/diagonal buy-up • requires the new optimizer • Market-based RM • pessimistic unless competitor RM itself is modeled