1. H OPPERSTA D C ONSULTIN G Market/Airline/Class (MAC) Revenue Management AGIFORS RM 2003 Hopperstad June 03

2. H OPPERSTA D C ONSULTIN G 2 RM modeling assumptions a short (public) history 80’s – leg/fare class demand independence  4 to 6% 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

3. H OPPERSTA D C ONSULTIN G 3 Yet, everyone here 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, full-up market-based RM is a giant step –it is proposed here that a small next step is to assume independent market/airline/class demand RM modeling assumptions

4. H OPPERSTA D C ONSULTIN G 4 Subjects Background: PODS Functional form Some results Potential real-world application Lines of inquiry

5. H OPPERSTA D C ONSULTIN G 5 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 Background: PODS passenger origin/destination simulator

6. H OPPERSTA D C ONSULTIN G 6 Leg/class baseline: Expected Marginal Seat Revenue (EMSRb) Three path/class RM systems available in the current version of PODS –Network bidprice (NetBP) –Prorated bidprice (ProBP) –Displacement adjusted virtual nesting (DAVN) Background: PODS

7. H OPPERSTA D C ONSULTIN G 7 EMSRb 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 Background: PODS

8. H OPPERSTA D C ONSULTIN G 8 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 Background: PODS

9. H OPPERSTA D C ONSULTIN G 9 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 (EMSRb) 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 Background: PODS

10. H OPPERSTA D C ONSULTIN G 10 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 (EMSRb) leg/class optimizer to set availability Background: PODS

11. H OPPERSTA D C ONSULTIN G 11 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 –a MAC is marked as closed only if all paths closed Assumption: all spill is contained within a MAC until all paths (of index airline) are closed for the class Architecture

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

13. H OPPERSTA D C ONSULTIN G 13 Path/class availability solver (DAVN) Optimizers yes no allocate original MAC forecasts to associated path/classes and create virtual classes using final MAC bidprices solve for leg/virtual class availability 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 any new path/classes closed? quit recalculate leg/virtual class demand

14. H OPPERSTA D C ONSULTIN G 14 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 Additional technology

15. H OPPERSTA D C ONSULTIN G 15 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) Additional technology

16. H OPPERSTA D C ONSULTIN G 16 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 Additional technology

17. H OPPERSTA D C ONSULTIN G 17 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 Some results 20 CITIES HUB AL 1 HUB AL 2 20 CITIES

18. H OPPERSTA D C ONSULTIN G 18 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 (EMSRb) Results quoted as % revenue gains compared to both airlines using EMSRb Results 1

19. H OPPERSTA D C ONSULTIN G 19 Results 1 NetBPProBPDAVN +MAC revenue gain

20. H OPPERSTA D C ONSULTIN G 20 Airlines 1 and 2 follow a sequence of RM improvements using DAVN –start with both using EMSRb –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 EMSRb Results 2

21. H OPPERSTA D C ONSULTIN G 21 Results 2 AL1 DAVNAL2 DAVNAL1 MACAL2 MAC revenue gain

22. H OPPERSTA D C ONSULTIN G 22 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 EMSRb Results 3

23. H OPPERSTA D C ONSULTIN G 23 Results 3 NetBPProBPDAVN revenue gain Note: Mac spill gain dominated by spill-in compared to spill-out

24. H OPPERSTA D C ONSULTIN G 24 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 Potential real-world application of MAC

25. H OPPERSTA D C ONSULTIN G 25 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 Lines of inquiry