©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…”

Talus Solutions. CONFIDENTIAL. A Wealth of Competing Business Models “e-Travel Agent” Direct Airline Sales Distressed Inventory Ticket Auction Buyer Names Price Dutch Auction …

Talus Solutions. CONFIDENTIAL. 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.

Talus Solutions. CONFIDENTIAL. 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.

Talus Solutions. CONFIDENTIAL. 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.

Talus Solutions. CONFIDENTIAL. 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 a particular flight, but if it increases consumer expectation of distressed seat availability, it may be destructive...

Talus Solutions. CONFIDENTIAL. Model Assumptions For each flight, an airline initially sells full fare seats and has the option to offer unsold seats at a“distressed” fare. C = Capacity r f = Full Fare r d = Distressed Fare r d < r f b = Maximum Seats offered at Distressed Fare (b < C) The airline does not “reserve” any seats to sell at the distressed fare.

Talus Solutions. CONFIDENTIAL. 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. p f ( U - r f ) > p d (U - r d ) ---> Seek to purchase full fare p f ( U - r f ) > p d (U - r d ) > 0 ---> Seek to purchase distressed 0 > p d (U - r d ) ---> Don’t seek to purchase Where: p f = Probability of getting a full fare seat p d = Probability of getting a distressed fare seat

Talus Solutions. CONFIDENTIAL. Book Distressed Customer Choice Model U > r* ---> Seek to book full fare r* > U > r d ----> Seek to book distressed fare U Do not book Where: r* = (p f r f - p d r d ) / (p f - p d ) 0 rdrd rfrf r* U Book Full Fare f(U)

Talus Solutions. CONFIDENTIAL. Calculating Demand Define D(x) = Number of potential customers with U > x f i = Unconstrained demand for fare type I L i = Realized load for fare type i Then: d f = D(r*) d d = D(r d ) - D(r*) L f = min(d f C) L d = min(d d, b, C - L f )

Talus Solutions. CONFIDENTIAL. Calculating Demand 0 rdrd rfrf r*U d f(U) dfdf

Talus Solutions. CONFIDENTIAL. A Dynamic Model... Assume that potential customers set p i = 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) = [p f (k)r f - p d (k)r d ]/ [p f (k) - p d (k)] d f (k+1) = D[r*(k+1)] d d (k+1) = D[r d (k+1)] - D[r*(k+1)] L f (k+1) = min[d f (k+1), C]L d (k+1) = min[d d (k+1), b, C - L f (k+1)] p f (k+1) = L f (k+1)/ d f (k+1) p d (k+1) = L d (k+1)/ d d (k+1)

Talus Solutions. CONFIDENTIAL. Specific Example C = 100r f = 75r d = 50 D(U) = U for 0 < U < 150; 0 otherwise How does Total Revenue (TR) vary over time as a function of b? Total Revenue Iteration Revenue ($) b = 0 b = 10 b = 20 b = 25 b = 50

Talus Solutions. CONFIDENTIAL. Equilibrium Total Revenue Per Flight Depends strongly on the distressed booking limit, b: bTotal Revenue * * *Periodic cases -- average total revenue

Talus Solutions. CONFIDENTIAL. 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

Talus Solutions. CONFIDENTIAL. 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

Talus Solutions. CONFIDENTIAL. Lessons Learned The 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

Talus Solutions. CONFIDENTIAL. 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.