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