E-Commerce Revenue Management Challenges

1. E-Commerce Revenue Management Challenges Robert L. Phillips

2. 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…”

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

4. 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.

5. 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.

6. 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.

7. 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...

8. 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 • 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.

9. 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

10. Customer Choice Model • U > 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) f(U) Book Distressed Book Full Fare rf rd r* 0 U

11. 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 )

12. Calculating Demand f(U) dd df rd rf 0 r* U

13. 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)

14. Specific Example 7000 • C = 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 6000 5000 b = 0 4000 b = 10 b = 20 Revenue ($) b = 25 b = 50 3000 2000 1000 0 26 1 16 21 6 11 Iteration

15. 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

16. 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

17. 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

18. 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

19. 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.