HOPPERSTADCONSULTING
Market/Airline/Class (MAC) Revenue Management
AGIFORS RM 2003
HopperstadJune 03
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HOPPERSTADCONSULTING
RM modeling assumptionsa 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
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HOPPERSTADCONSULTING
• 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
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Subjects
• Background: PODS
• Functional form
• Some results
• Potential real-world application
• Lines of inquiry
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HOPPERSTADCONSULTING
• 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: PODSpassenger origin/destination simulator
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HOPPERSTADCONSULTING
• 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
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HOPPERSTADCONSULTING
• 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
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HOPPERSTADCONSULTING
• 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
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• 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
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HOPPERSTADCONSULTING
• 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
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HOPPERSTADCONSULTING
• 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
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• 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
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• 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
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HOPPERSTADCONSULTING
• 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
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HOPPERSTADCONSULTING
• 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
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HOPPERSTADCONSULTING
• 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
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• 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
HUBAL 1
HUBAL 2
20 CITIES
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• 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
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Results 1
-1.50%
-1.00%
-0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
Airline 1
Airline 2
NetBP ProBP DAVN
+MAC +MAC +MAC
reve
nue
gain
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HOPPERSTADCONSULTING
• 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
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HOPPERSTADCONSULTING
Results 2
-1.00%
-0.50%
0.00%
0.50%
1.00%
1.50%
2.00%
Airline 1
Airline 2
AL1 DAVN AL2 DAVN AL1 MAC AL2 MAC
reve
nue
gain
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• 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
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Results 3
NetBP ProBP DAVN0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
MAC spill
MAC
Optimizer
reve
nue
gain
Note: Mac spill gain dominated by spill-in compared to spill-out
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• 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
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HOPPERSTADCONSULTING
• 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