MIT
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Pricing Challenges:
ePODS and Reality
Dr. Peter P. Belobaba
16.75J/1.234J Airline Management
May 8, 2006
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PODS: Passenger Choice of Path/Fare
y Given passenger type, randomly pick for each
passenger generated:
ƒ Maximum “out-of-pocket” willingness to pay
ƒ Disutility costs of fare restrictions
ƒ Additional disutility costs associated with “re-planning” and path
quality (stop/connect) costs
y Screen out paths with fares greater than this
passenger’s WTP.
y Assign passenger to feasible (remaining) path/fare
with lowest total cost.
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Willingness to Pay (WTP)
Probability ( pay at least f ) = min[1, e
− log(2) * ( f − basefare)
]
( emult − 1) * basefare
With: basefare = Q fare for leisure passengers
= 2.5 * Q fare for business passengers
And: emult implies
• approx 20% of leisure passengers will pay higher fare
• most business passengers will pay Y fare if necessary
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E-PODS Baseline Fare Structure
Fare Price
Code Level
Y
M
B
Q
Advance Sat. Night
NonPurchase Min. Stay Refundable
$350
-$200 7 day
$150 14 day
$100 21 day
-Yes
Yes
Yes
--Yes
Yes
Change
Fee
---Yes
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Fare Class Restriction Disutilities
y Disutility costs associated with the restrictions of each
fare class are added to the fare value to determine the
choice sequence of a given passenger among the
classes with fare values less than his/her WTP.
y The restrictions are:
ƒ R1: Saturday night stay (for M, B and Q classes),
ƒ R2: cancellation/change penalty (for B and Q classes),
ƒ R3: non-refundability (for Q class).
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Fare Restriction Disutilities
y These coefficients have been “tuned” with structured
fares so that on average* business and leisure
passengers have respectively a Y/M/B/Q and a Q/B/M/Y
choice sequence, as shown on the next two slides.
y *The following slides represent the mean disutilities for an average
passenger. The actual disutility value for an individual passenger is a
random number taken from a normal distribution centered on the
mean disutility value.
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Business Passenger Generalized Costs
500
450
400
350
REST 3
REST 2
REST 1
FARE
300
250
200
150
100
50
0
Y
M
B
Q
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Leisure Passenger Generalized Costs
400
350
300
250
REST 3
REST 2
REST 1
FARE
200
150
100
50
0
Y
M
B
Q
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Interpretation of Cost Parameters
y Assumed MAX PAY values:
ƒ Virtually all business passengers will pay Y fare if necessary
ƒ Most leisure passengers will not buy B, very few will buy M
y Assumed relative restriction disutility costs:
ƒ Average business passenger finds fares with more restrictions
less attractive
ƒ Even with restrictions, most leisure passengers prefer Q fare
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Fare Simplification:
Less Restricted and Lower Fares
y Recent trend toward “simplified” fares – compressed
fare structures with fewer restrictions
ƒ Initiated by some LFAs and America West, followed by Alaska
ƒ Most recently, implemented in all US domestic markets by Delta,
matched selectively by legacy competitors
y Simplified fare structures characterized by:
ƒ No Saturday night stay restrictions, but advance purchase and
non-refundable/change fees
ƒ Lower fare ratios from highest to lowest available fares, typically
no higher than 4:1 in affected US domestic markets
ƒ Revenue management systems still control number of seats sold
at each fare level
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Example: BOS-ATL Simplified Fares
Delta Air Lines, April 2005
One Way Bkg Advance Minimum
Fare ($) Cls Purchase
Stay
$124
T 21 days
0
$139
U 14 days
0
$184
L
7 days
0
$209
K
3 days
0
$354
B
3 days
0
$404
Y
0
0
$254
$499
A
F
0
0
0
0
Change
Fee?
$50
$50
$50
$50
$50
No
Comment
Non-refundable
Non-refundable
Non-refundable
Non-refundable
Non-refundable
Full Fare
No
No
First Class
First Class
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Traditional Leg-Based RM Approach
y Leg RM: EMSRb Seat Protection
ƒ Unconstraining and forecasting of bookings to come by flight leg
and fare class, based on historical bookings
ƒ Leg-based Expected Marginal Seat Revenue protection algorithm
for nested booking limits applied to fare classes
ƒ Re-optimization of booking limits 16 times before departure
y Concerns about traditional leg-based RM models
ƒ As restrictions are removed, more passengers buy lower fares
and fewer bookings are recorded in higher classes
ƒ Inadequate protection leads to “spiral-down” in unrestricted fares
y Is this a concern in semi-restricted fare structures?
ƒ Very few examples of fully unrestricted fares in practice
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LEG RM SIMULATIONS:
Impacts of Fare Restriction Removal
2 carriers, single market, both use EMSRb leg RM controls
6 fare classes, 3.4:1 fare ratio:
Class
Fare
1
2
425.00
310.00
3
4
200.00
5
175.00
6
150.00
125.00
BASE CASE: Fully Restricted Fares
Fare Class
AP
MIN Sat
Night
Chg
Fee
NonRefund
1
0
0
0
0
2
3
0
1
0
3
7
1
0
0
4
10
1
1
0
5
14
1
1
1
6
21
1
1
1
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Steps Toward Fare Simplification
y From fully restricted BASE, simulate impacts of
simplified restrictions and/or AP rules (separately):
ƒ
ƒ
ƒ
ƒ
Remove Advance Purchase Rules (only)
Remove Saturday Night Min Stay restriction (only)
Remove ALL restrictions but keep AP Rules
Remove ALL restrictions and AP Rules
y Assess impacts of each simplification on:
ƒ Total flight revenues
ƒ Fare class mix
ƒ Revenue gain performance of Leg-Based RM (EMSRb)
y When does “spiral down” make traditional Leg RM
controls ineffective?
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Revenue Impact of Each “Simplification”
65,000
60,000
.5%
50,000
.8%
-16
-0
55,000
.6%
-29
45,000
40,000
%
-45
35,000
30,000
Fully
Restricted
Remove AP
Remove Sat
Night Min Stay
Remove All
Restr, Keep
AP
Remove All
Restr and AP
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Loads by Fare Class
100
88.1
87.8
90
81.6
79.8
82.7
80
70
60
50
40
FC 6
30
FC 5
20
FC 4
FC 3
10
FC 2
0
Fully Restricted
Remove AP
Remove Sat
Night Min Stay
Remove All
Restr, Keep AP
Remove All Restr
and AP
FC 1
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Revenues by Fare Class
70,000
60,000
50,000
40,000
30,000
FC 6
FC 5
20,000
FC 4
10,000
FC 3
FC 2
0
Fully
Restricted
Remove AP
Remove Sat
Night Min Stay
Remove All
Restr, Keep
AP
Remove All
Restr and AP
FC 1
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Effectiveness of Traditional Leg RM
Percentage improvement of EMSRb over FCFS
65,000
8.2%
9.9%
60,000
EMSRb
55,000
FCFS
11.8%
50,000
6.7%
45,000
40,000
0.1%
35,000
30,000
Fully
Restricted
Remove AP
Remove Sat Remove All Remove All
Night Min
Restr, Keep Restr and AP
Stay
AP
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Existing Airline RM Systems Need to be
Modified for This New Environment
y RM systems were developed for restricted fares
ƒ Assumed independent fare class demands, because restrictions
kept full-fare passengers from buying lower fares
ƒ With unrestricted fares, passengers buy lowest available fare
y Without modification, these RM systems do not
perform well in less restricted fare structures
ƒ Unless demand forecasts are adjusted to reflect potential sell-up,
high-fare demand will be consistently under-forecast
ƒ Optimizer then under-protects, allowing more “spiral down”
y RM system limitations are affecting airline revenues
ƒ Existing systems, left unadjusted, generate high load factors but
do not maximize revenues
ƒ Many airlines are currently using manual overrides
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Current RM Challenge is To Find New
Forecasting and Optimization Models
y Less restricted fare structures require forecasting of
passenger choice and “willingness to pay”
ƒ Instead of forecasts by product/restriction
y The new RM problem is much more complicated than
independent class demand RM environment:
ƒ Affected by passengers’ actual willingness to pay, and ability of
airline to estimate this willingness to pay
y Existing Network RM systems also need to be
modified for multiple fare structures
ƒ How to control seat availability in unrestricted fare domestic
markets while managing seats in more traditional fare markets
ƒ Seats shared by passengers in both types of markets
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MEM Proposed Structure
Fare Price
Code Level
Y
M
B
Q
Advance Sat. Night
NonPurchase Min. Stay Refundable
$350
-$200 7 day
$150 14 day
$100 21 day
---Yes
Change
Fee
-Yes
Yes
Yes
-Yes
Yes
Yes
• COMPETING AIRLINES MAY DECIDE TO MATCH CONDITIONS OF
• M class ONLY or B class only (partial match)
• BOTH M and B classes (complete match of MEM fare structure)
• NEITHER M nor B classes (initial fare structure remains intact)
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