Revenue Management with Restriction-free Pricing
AGIFORS Reservations and Yield Management 2003
Honolulu, June 2-5, 2003
Shankar Mishra & Vish Viswanathan
2
Revenue Management Environment
Y
Y6S
KVE14N
KSE14N
KFSE21N
KUE14N
SVVE14N
SA3S
WGE30N
WSE30N
WGE14AN
Fare Class Alignment
BookingLimits
AverageRevenue
Y
K
S
W
Availability is controlled by Validity of Fares Fare Restrictions Inventory Control
Fare Restrictions
3
Pricing Restrictions
Saturday Night Stay
Min Stay Requirements
Max Stay Requirements
Adv. Purchase Restrictions
Routing Restrictions
Exchange, Cancellation
Penalties
Point of Sale Restrictions
Distribution Channel
Restrictions
Simplification starts in a rather unexpected manner
…
Proliferation of Low Cost Carriers
4
“How do you get to be a millionaire? Be a billionaire and start an airline.”
Sir Richard Branson
5
Low Cost Carriers
2000
1992
1995
1995
1985
2002
Enhanced Product, One-way Fares
The Pioneer (since 1971)Reduced number of fare types (<10)
Europe
6
Low Cost Carriers Effect
A simpler pricing structure
Changes to “pricing fences” None at some airlines None for a price range
Non-refundable bookings Exchange fee Possibility of a higher fare
Lower distribution costs
Inventory Control with revenue management
Price is the main differentiator in market segmentation
7
From LCC to Majors
LCC aren’t the only ones offering fares with fewer restrictions
Majors from Europe: Alitalia, Lufthansa, British Airways
Majors within US New one-way fares with minimal restrictions from Delta, United,
US Airways, Hawaiian Two main reasons
pressure from Southwest, jetBlue, AirTran Research into launching their own LCC
Time to take a closer look into applicability of traditional revenue management techniques
Role of Revenue Management
8
“If I can get a £7 flight to somewhere within 200 miles of Venice, well I’ll *@%* take it. Seven
quid, I don’t care where I *@%* go.”
Sir Bob Geldof
9
Role of Revenue Management
No Pricing RestrictionsAll non-refundable sales
One fare per O&D,Cabin at a time
When to close the current available fare in any market?
After x number of bookings?At y days before departure?
Role of Revenue Management
being re-defined
10
Traditional RM Approach
Assumption of demand independence among all classes within a cabin
Protection for higher classes assumes “Infeasibility of Sell-down”
Market Segmentation Assumptions Segment restrictions included in pricing definitions Independence among market segments (distribution channel,
point-of-sale) Effect of external conditions on market segmentation
11
Issues with Traditional RM
Total demand < Capacity High dilution possibility
High Demand Flights Over-estimation of demand in higher classes – high spoilage,
some dilution Under-estimation of demand in higher classes – high dilution,
some spoilage
Even with a perfect total demand forecast – arrival order will cause dilution & spoilage
Demand from multiple classes may materialize in one time-period buying the only available fare in marketplace
Any dilution may also give rise to spoiled seats which are protected for higher classes
Roadmap to a new solution
12
“Informed decision-making comes from a long tradition of guessing and then blaming others for
inadequate results.”Scott Adams
13
Solution Ingredients
Inventory Control Given a pricing structure, how many seats to sell at each price
(Segment, Compartment)?
Optimality Maximizes Revenue Maximizes Profit (variable cost of sale is factored in) Minimizes Dilution & Spoilage
Current Practice Closure of a price point by “Days-to-departure”
BookingLimits
TimeLimits
A fare class is closed if bookings reach a pre-defined limitClose a fare class at “N” days before departure
14
Solution Expectations
$0
$10
$20
$30
$40
$50
$60
$70
$80
Days to Departure
Fa
re
0
10
20
30
40
50
60
70
80
90
Exp
Bo
oki
ng
s
Fare Structure
Cum. Exp.
Bookings
W
K
B
S
Fare Structure / Exp Bookings
Class Closure
W: either at day 84 or after10
bookings
K: either at day 35 or after 27
bookings
B: either at day 14 or after 60
bookings
S: either at day 5 or after 72 bookings
15
Influencing Factors
Price points and related bookings Historic and current, if applicable
Current price points and observed bookings
Inventory controls
Grouping of historical data – Seasonality
Relationship between Price & Demand with respect to Time
Available capacity to optimize
External factors Competitor pricing, load factors, schedule etc
16
Optimization Module
Price/Bookings
0
5
10
15
20
25
30
112 96 84 75 50 40 25 10
Price Points
Obs
. Boo
king
s
Demand Example
0
1020
30
40
5060
70
80
0 1 2 3 4 5 14 21 28 35 42 70 84 98 182
Days to Departure
Dem
and
$75
$10
$84
$40
$25Open/Close Periods
Price-Demand Relationship
A Dynamic Programming Formulation
Rn(Space,Price,Demand) = Max0kSpacern(k) + Rn-1(Space – k, Price, Demand)
Function of Allocation kRe-calculated after each
allocationRevenue/Profit
Optimal Booking Limits at each Price Point
17
Optimization Inputs
Demand Example
0
1020
30
40
5060
70
80
0 1 2 3 4 5 14 21 28 35 42 70 84 98 182
Days to Departure
Dem
and
$75
$10
$84
$40$25
Demand estimation for each price point at a point in time (days to departure)
Concept of Sell-up and Sell-down Demand for a $50 fare deduced from observed
bookings at all <$50 fare in a time-band Sell-up Demand for a $50 fare deduced from observed
bookings at all >$50 fare in a time-band Sell-down
Calibration of Sell-up & Sell-down curves Depends upon Proximity of Fares Relationship with days to departure
Unconstraining at cabin level only
18
Post-Optimization Analysis
Calculation of Time Limits Booking and Time Limits are in sync at each
optimization Time Limits may be re-calculated in between
optimization points
Ability to enhance price structure periodically Factors being changing booking patterns &
competitor activities
Estimation of Bid Price Curve from optimization OA Fares included to better estimate bid prices
Generation of an Initial Bid Price Curve based on historical data
Updates to Bid Price Curve with every optimization
Possibility of changing the price structure in marketplace
0
20
40
60
80
100
120
Y S B K W
Class
Boo
king
Lim
it
$0$10
$20
$30$40
$50
$60$70
$80
0 1 2 3 4 5 6 711 14 21 28 35 42 70 84 98
182
210
Days to Departure
Time Limits
19
Post-optimization Analysis
0
10
20
30
40
50
60
70
80
Days to Departure
Fare
0
10
20
30
40
50
60
70
80
90
100
Exp
. Boo
king
s
Cum. Exp. Bookings
Bid Price Curve
Price Structure Update
Enhanced Price Structure
Updates to Booking &
Time Limits
20
Additional Complexity
Not all classes in the cabin may follow “restriction-free pricing”
Calculation of available space for these group of classes
Moving Curtain Aircrafts
Moving Curtain Aircrafts with “Variable Seating Configuration”
Group Handling & Integration with RM
Enhancements with External Data Competitor Pricing, Load Factors, Schedule etc.
21
Measuring Performance
Analysis of Price Structure offered in the marketplace on a post-departure basis
Comparison with post-departure Bid Price Curve Loss of opportunity on the pricing front
Measuring Revenue & Profit against pre-defined targets
Initial target based on historical data (grouped by entity, seasonality etc.)
Continuous revision of target based on observed booking & competitor activities
Provision of Feedback to Revenue Management Solution
Improved estimation of price-demand relationship w.r.t. time Continuous updates to Bid Price Curve linked to revenue
opportunity
22
Acknowledgements
Suzanne DonnellyChris Binnion
John WardmanJonathan Downes
Donna ClarkstoneAndy Bosworth
Stuart Mason, Prism Inc.
Dr. Miguel AnjosChristine Currie
Questions?
23
“It never occurred to them that there was a fourth class out there called the human race who
just wanted to fly at the lowest fare.”Sir Freddie Laker