©2000 Talus Solutions, Inc. All Rights Reserved. March 23, 2000 E-Commerce Revenue Management...

©2000 Talus Solutions, Inc. All Rights Reserved.

March 23, 2000

E-Commerce Revenue Management Challenges

Robert L. Phillips

Talus Solutions. CONFIDENTIAL.

My Prediction

“Based on the power of exponential growth, by the year 2025, 375% of all airline tickets sold in the world will be sold via the Internet…”


A Wealth of Competing Business Models

•“e-Travel Agent”

•Direct Airline Sales

•Distressed Inventory

•Ticket Auction

•Buyer Names Price

•Dutch Auction

•…


What Is the Role of Revenue Management in an Internet Age?

Same as it ever was:

Determine what prices to be offering through what channels for what products to which market segments at each time in order to maximize profit.

But this is even more complex in a multi-channel environment.


Sales of Distressed Inventory

The Internet provides a convenient channel to sell “distressed” inventory and a number of e-commerce business models (both in and outside the airlines) are based on this concept.

In the airlines, selling “distressed” inventory at deep discounts presents consumers with a choice:

1. Purchase at full fare with high probability of receiving a booking.

2. Wait for “distressed” inventory to go on sale with a lower probability of receiving a booking.


A Dynamic Game

Optimal Airline Policy is based on consumer expectations:

1. If consumers expect a small likelihood of being able to book distressed seats, they are far more likely to book full fare.

2. If consumers expect a high likelihood of being able to book distressed seats, they are much less likely to book full fare.

Thus, optimal airline policy must be dynamic and be as much about managing customer expectations as about flight-by-flight optimization.


Optimizing Distressed Inventory Sale

“Simple view”:

• Identify flights that are likely to have unsold inventory.

• Allow that inventory to be sold late at a deeply discounted fare.

This policy might increase revenue on aparticular flight, but if it increases consumer expectation of distressed seat availability, it may be destructive...


Model AssumptionsFor each flight, an airline initially sells full fare seats and has the option to offer unsold seats at a“distressed” fare.

C = Capacity

rf = Full Fare

rd = Distressed Fare rd < rf

b = Maximum Seats offered at Distressed Fare (b < C)

The airline does not “reserve” any seats to sell at the distressed fare.


Model Assumptions -- Consumer Behavior

Each Potential Customer has a (monetized) “Utility of Travel” U > 0. Potential Customers determine their buying behavior by maximizing their expected utility. Potential customers will make one of three decisions, based on their Utility of Travel.

pf ( U - rf ) > pd (U - rd ) ---> Seek to purchase full fare

pf ( U - rf ) > pd (U - rd ) > 0 ---> Seek to purchase distressed

0 > pd (U - rd ) ---> Don’t seek to purchase

Where: pf = Probability of getting a full fare seat

pd = Probability of getting a distressed fare seat


Book Distressed

Customer Choice ModelU > r* ---> Seek to book full fare

r* > U > rd ----> Seek to book distressed fare

U < rd ---> Do not book

Where: r* = (pf rf - pd rd) / (pf - pd)

0 rd rf r* U

Book Full Fare

f(U)


Calculating Demand

Define D(x) = Number of potential customers with U > x

fi = Unconstrained demand for fare type I

Li= Realized load for fare type i

Then: df = D(r*) dd = D(rd) - D(r*)

Lf = min(df C) Ld = min(dd, b, C - Lf )


Calculating Demand

0 rd rf r* U

dd

f(U)

df


A Dynamic Model...

Assume that potential customers set pi = the fraction of unconstrained demand in each class that is accommodated.

Then, all the pieces are in place for a dynamic model of customer behavior:

r*(k+1) = [pf(k)rf - pd(k)rd]/ [pf(k) - pd(k)]

df(k+1) = D[r*(k+1)] dd(k+1) = D[rd(k+1)] - D[r*(k+1)]

Lf(k+1) = min[df(k+1), C] Ld(k+1) = min[dd(k+1), b, C - Lf(k+1)]

pf(k+1) = Lf(k+1)/ df(k+1) pd(k+1) = Ld(k+1)/ dd(k+1)


Specific ExampleC = 100 rf = 75 rd = 50

D(U) = 150 - U for 0 < U < 150; 0 otherwise

How does Total Revenue (TR) vary over time as a function of b?

Total Revenue

0

1000

2000

3000

4000

5000

6000

7000

1 6 11 16 21 26

Iteration

Rev

en

ue

($

)

b = 0b = 10b = 20b = 25b = 50


Equilibrium Total Revenue Per Flight

Depends strongly on the distressed booking limit, b:

b Total Revenue

0 5625

10 5375

20 5125

25 3750*

50 4375*

*Periodic cases -- average total revenue


Initial Model Insights• Effective management of “distressed” inventory

sales will require understanding and modeling the evolution of customer expectations

• Complex non-linear dynamic behavior is possible

• Forecasting with incorporating these effects will likely be extremely difficult.

• After the initial benefits are achieved -- “pure” strategies seem to be generally dilutionary

• “Mixed strategies” may turn out to be optimal


Research Directions

• More robust consumer model including booking time preference and evolution

• Incorporate richer models of subjective booking probability formation

• Further analysis and use of real-word data


Lessons LearnedThe rise of e-commerce will present strikingly new challenges and opportunities for revenue management analysts:

• New analytical techniques and models required to manage new selling models

• More focus on pricing dynamics rather than availability management

• Need for new customer segmentations

• Need for better understanding and representations of customer preferences and behavior

• Need to support a variety of business models

• Need to include variable channel costs and effectiveness in RM analyses

• Availability of extensive “new” data on customer behavior and preferences


The Bottom Line

The Internet is more than just an exciting and revolutionary sales channel for airlines…

… it is also a lifetime full employment act for Revenue Management analysts.

©2000 Talus Solutions, Inc. All Rights Reserved. March 23, 2000 E-Commerce Revenue Management...

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