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Welcome

Welcome. Yield Management Jonathan Wareham j.wareham@esade.edu. HealthCare/ Hospitals. Insurance/ banking. Sports Parks. Entertainment. Car rental. Freight, Cargo. Manufact. Rail Transp. Hotels. Tour Operators. Retailers. Airlines. 1980. 1985. 1990. 1995. RM Evolution.

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Welcome

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  1. Welcome Yield Management Jonathan Wareham j.wareham@esade.edu

  2. HealthCare/ Hospitals Insurance/ banking Sports Parks Entertainment Car rental Freight, Cargo Manufact. Rail Transp. Hotels Tour Operators Retailers Airlines 1980 1985 1990 1995 RM Evolution Telco/ISP Cruise lines Energy Media 2000

  3. Fixed Prices P $1.00 1 Coke Q

  4. Fixed Prices Consumers Surplus Dead Weight Loss MC

  5. Get a little more revenue

  6. 2nd Degree Price Discrimination • “product line pricing”, “market segmentation”, “versioning” • Gold Club, Platinum Club, Titanium Club, Synthetic Polymer Club • First Class, Business Class, World Traveler Class • Professional Version, Home Office

  7. 3rd Degree Price Discrimination • The practice of charging different groups of consumers different prices for the same product • Examples include student discounts, senior citizen’s discounts, regional & international pricing, coupons

  8. Maximize the Revenue ! Perfect (1st degree) Price Disc.

  9. Prefect Price Discrimination • Practice of charging each consumer the maximum amount he or she will pay for each incremental unit • Permits a firm to extract all surplus from consumers • Difficult: airlines, professionals and car dealers come closest

  10. Caveats: • In practice, transactions costs and information constraints make this is difficult to implement perfectly (but car dealers and some professionals come close). • Price discrimination won’t work if you cannot control three things: • Preference profiles • Personalized billing; (anonymous transactions lesson seller’s discriminatory power over consumers) • Consumer arbitrage

  11. Conclusions • Internet double edged sword: • Consumers enjoy lower search costs, but… • eMarketers have superior tools to register your consumption patterns and price sensitivity • The end of fixed pricing??? • Fixed pricing as an institution only 100 years old!! • Developed in response to large scale economies/production models….with standard products !!!!

  12. Vertical Differentiation High Price Low Quality

  13. Tickets; $7.95 $1.00 Discount for Children & Seniors Tickets; $6.95 $1.00 Extra for Middle Aged People ...Decisions Are Not Always “Rational”

  14. More Acceptable Pricing Product-Based Open Discretionary Discounts and Promotions Rewards Less Acceptable Pricing Customer-Based Hidden Imposed Surcharges Penalties Price Perception Issues are Complex...

  15. RM coming of age • Airline deregulation in the U.S. • People Express vs. American Airlines • Edelman Award: RM for AA $1.4 billion in 3 years • virtually every airline has implemented RM • National Car Rental (vs. GM) • Edelman Award: RM for SNCF • AA: $1 billion incremental revenues from RM • Marriott Int’l RM: 4.7% increase in room revenue • Deregulation Europe: telecom, media,energy … • e-distribution supports dynamic pricing & profiling • Dell, Amazon & Coca Cola experiment dynamic pricing • RM spans wide range of industries … 1978: 1985: 1992: 1997: 1999: 2000-01: 2003:

  16. YM: Where and When? • Perishable: impossible to store excess resources • Choose now: future demand is uncertain (how many rooms to sell at low price) • Customer segmentation with different demand curves • Same unit of capacity can be used to deliver different services • Producers are profit driven and price changes are accepted socially

  17. Major Types • Revenue Management (EMSR) • Peak-Load Pricing • Markdown Management • Customized Pricing • Promotions Pricing • Dynamic List Pricing • Auctions

  18. Revenue Management • Set of techniques use to manage • Constrained, perishableinventory (time) • When customer willingness to pay increases towards departure • Applications: • Airlines, Hotels, Car Rentals, News Vendors • Main techniques: Open and close certain rate categories (rate fences) based on historical probabilities and forecasts of future demand

  19. The RM Challenge Arrivals of high paying customers… Closer to departure! Arrivals of low paying customers…Earlier!

  20. Peak-Load Pricing • Tactic of varying the price of constrained and perishable capacity to reflect imbalances between supply and demand • Based on changing prices only, not availability like RM. No perishable inventory • Simple= when demand increases, raise prices • Industries= utilities (electricity, telephones) theme parks, toll bridges, theatres (afternoon showings)

  21. Markdown Management • Techniques used to clear excess, perishable inventory over time • Customer demand decreases over time (opposed to RM) • Used in retailing of fashion apparel and consumer electronics where there is a high obsolescence

  22. Customized Pricing • Occurs when the seller has the opportunity to offer a unique price to a buyer • Equivalent to first degree price discrimination • Used by car dealers, professional services, industrial sales, made to order manufacturing, person to person negotiation of non-standardized products

  23. Promotions Pricing • Similar to markdown management • Portfolio of tools to address different customer segments. • Example Automobile Sales • Low income like cheap financing and low down payment • High income like cash back, additional add-ons, services warranties/agreements

  24. Dynamic List Pricing • Dynamically move prices up and down according to perceived changes in demand. • Products not constrained, can reorder more. • Not traditionally used because of high menu costs • Now used in Internet and traditional retailing due to new technologies.

  25. Auctions • Variable pricing mechanisms • Often used for instances when prices are not easily determined • English • First price sealed bid • Vickrey • Dutch

  26. The RM Challenge Arrivals of high paying customers… Closer to departure! Arrivals of low paying customers…Earlier!

  27. Expected Marginal Seat Revenue • “ESMR” Kernel in many YM systems • Peter Belobabba, MIT • Belobaba, P. “Application of a Probabilistic Decision Model to Airline Seat Inventory Control,” Operations Research, vol 37(2) 1989.

  28. EMSR a simple example • Hotel; 210 rooms • Business Customers = 159$ night • Leisure Customers = 105$ night • We are now in February, the hotel has 210 rooms available for March 29. • Leisure Customers book earlier • Business Customers book later • How many rooms to sell at low price now? • How many to save to try and sell a high price later? • What if we don not sell them all at 159$ - then we lost 105$ per room!!!!

  29. Terms • Booking limit: Maximum number of rooms to be sold at low price • Protection level: Number of rooms to be saved for the business customers who arrive later • Booking limit = 210 – protection level

  30. Depiction: What should Q be? 210 rooms Q+1 rooms protected (protection level) Q 210- (Q-1) rooms sold at discount (booking limit)

  31. Decision Tree Revenue Yes – sell (Q+1) room now 105 $ Lower protection level from Q+1 to Q? Sold at full price later 159 $ No – protect (Q+1) rooms Not sold by March 29 0 $

  32. Historical Demand

  33. Decision Tree Revenue Yes – sell (Q+1) room now 105 $ Lower protection level from Q+1 to Q? 1-F(Q) 159 $ No – protect (Q+1) rooms F(Q) 0 $

  34. Calculation (1-F(Q))($159) + F(Q)($0) = (1-F(Q))*($159) Therefore we should lower booking limit to Q as long as (1-F(Q))*($159)<=$105 Or F(Q)>=($159-$105)/$159= 0.339

  35. Rational • Find smallest Q with a cumulative value greater than or equal to 0.339. • Optimal protection is Q=79 with a cumulative value of .341 • Booking limit: 210 -79 =131 • Save 79 rooms for business travlers • Sell 131 rooms for tourist travlers

  36. Overbooking • Lost revenue due to seats • Penalties and financial compensation to bumped customers • X = # of no-shows with distribution of F(x) • Y =number of seats overbooked • Airplane has S# of seats • We will sell S+Y tickets

  37. Overbooking Calculation • C = penalties and bad will caused by bumping customers • B represents the opportunity cost of flying with an empty seat (or the price of the ticket) • The optimal number of overbooked seats • F(Y) >= B/B+C

  38. Overbooking Example • # of customers who book but fail to show up are normally distributed mean=20 std.=10 • It costs $300 to bump a customer • Hotel looses $105 if it does not sell room at $105 • Overbooking b/b+c $105/($105+$300) = .2592

  39. Overbooking Example • From normal distribution we get • Φ(-.65)= 0.2578 & Φ(-.64) = 0.2611 • Take z*=-0.645 • Overbook Y=20-(0.645*10)=13.5 • Excel =Norminv(.2592, 20, 10) gives 13.5 • Round up to 14 means 210+14=224

  40. Overbooking metrics • Service level based: • P(denial) =0.05 • E[#denials]=2 • Etc. • Cost based: assign a cost to each and optimize Overbooking cost (airlines): • Direct compensation cost • Provision cost of hotel/meal • Reaccom cost (another flight/airline) • Ill-will cost (~ “lifetime customer value”)

  41. Overbooking Airlines Hotels Car rentals Education Manufacturing Media No Overbooking Restos Movies, shows Events Resort hotels Cruise lines Industries

  42. CRM & RM

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