Welcome Yield Management Jonathan Wareham [email protected].
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Transcript of Welcome Yield Management Jonathan Wareham [email protected].
RM Evolution
1980
AirlinesAirlines
1985
RailTransp.
RailTransp.
1990
HotelsHotels
Car rentalCar rental
2000
MediaMedia
EnergyEnergy
Cruise linesCruise lines
Telco/ISPTelco/ISP
1995
Tour Operators
Tour Operators
Freight,Cargo
Freight,Cargo
SportsParksSportsParks
EntertainmentEntertainment
HealthCare/Hospitals
HealthCare/Hospitals
Insurance/banking
Insurance/banking
Manufact.Manufact.
RetailersRetailers
P
Q
$1.00
1 Coke
Fixed Prices
P
Q
P
Q
Fixed Prices
Consumers Surplus
Dead Weight Loss MC
P
Q
P2
Q2
P3
P1
Q1 Q3
Get a little more revenue
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
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
P
Q
Maximize the Revenue !Perfect (1st degree) Price Disc.
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
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
1. Internet double edged sword:
• Consumers enjoy lower search costs, but…
• eMarketers have superior tools to register your consumption patterns and price sensitivity
2. 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 !!!!
Conclusions
Vertical Differentiation
Price
Quality
High
Low
...Decisions Are Not Always “Rational”
Tickets; $7.95
$1.00 Discount for Children &
Seniors
Tickets; $7.95
$1.00 Discount for Children &
Seniors
Tickets; $6.95
$1.00 Extrafor Middle Aged
People
Tickets; $6.95
$1.00 Extrafor Middle Aged
People
Price Perception Issues are Complex...
More Acceptable Pricing Product-Based Open Discretionary Discounts and
Promotions Rewards
Less Acceptable Pricing Customer-Based Hidden Imposed Surcharges Penalties
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 …
1985:
1978:
1992:
2000-01:
1997:
1999:
2003:
YM: Where and When?
1) Perishable: impossible to store excess resources
2) Choose now: future demand is uncertain (how many rooms to sell at low price)
3) Customer segmentation with different demand curves
4) Same unit of capacity can be used to deliver different services
5) Producers are profit driven and price changes are accepted socially
Major Types
Revenue Management (EMSR) Peak-Load Pricing Markdown Management Customized Pricing Promotions Pricing Dynamic List Pricing Auctions
Revenue Management
Set of techniques use to manage Constrained, perishable inventory (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
The RM Challenge
Arrivals of high paying customers…Closer to departure!
Arrivals of low paying customers…Earlier!
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)
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
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
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
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.
Auctions
Variable pricing mechanisms Often used for instances when prices
are not easily determined English First price sealed bid Vickrey Dutch
The RM Challenge
Arrivals of high paying customers…Closer to departure!
Arrivals of low paying customers…Earlier!
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.
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!!!!
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
Depiction: What should Q be?
210 rooms
Q+1 rooms protected (protection level)
210- (Q-1) rooms sold at discount (booking limit)
Q
Decision Tree
Revenue
105 $
Lower protection level from Q+1 to Q?
Yes – sell (Q+1) room now
No – protect (Q+1) rooms
Sold at full price later
Not sold by March 29
159 $
0 $
Historical DemandDemand for rooms at full
price
# days with
demand ProbabilityCumulative probability
0-70 12 9,8% 9,8%71 3 2,4% 12,2%72 3 2,4% 14,6%73 2 1,6% 16,3%74 0 0,0% 16,3%75 4 3,3% 19,5%76 4 3,3% 22,8%77 5 4,1% 26,8%78 2 1,6% 28,5%79 7 5,7% 34,1%80 4 3,3% 37,4%81 10 8,1% 45,5%82 13 10,6% 56,1%83 12 9,8% 65,9%84 4 3,3% 69,1%85 9 7,3% 76,4%86 10 8,1% 84,6%
above 86 19 15,4% 100,0%TOTAL 123 100,0% 100,0%
Decision Tree
Revenue
105 $
Lower protection level from Q+1 to Q?
Yes – sell (Q+1) room now
No – protect (Q+1) rooms
1-F(Q)
F(Q)
159 $
0 $
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)<=$105OrF(Q)>=($159-$105)/$159 = 0.339
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
Demand for rooms at full
price
# days with
demand ProbabilityCumulative probability
0-70 12 9,8% 9,8%71 3 2,4% 12,2%72 3 2,4% 14,6%73 2 1,6% 16,3%74 0 0,0% 16,3%75 4 3,3% 19,5%76 4 3,3% 22,8%77 5 4,1% 26,8%78 2 1,6% 28,5%79 7 5,7% 34,1%80 4 3,3% 37,4%81 10 8,1% 45,5%82 13 10,6% 56,1%83 12 9,8% 65,9%84 4 3,3% 69,1%85 9 7,3% 76,4%86 10 8,1% 84,6%
above 86 19 15,4% 100,0%TOTAL 123 100,0% 100,0%
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
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
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
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
Overbooking metrics
Service level based: P(denial) =0.05 E[#denials]=2 Etc.
Cost based: assign a cost to each and optimizeOverbooking cost (airlines): Direct compensation cost Provision cost of hotel/meal Reaccom cost (another flight/airline) Ill-will cost (~ “lifetime customer value”)
Industries
Overbooking Airlines Hotels Car rentals Education Manufacturing Media
No Overbooking Restos Movies, shows Events Resort hotels Cruise lines
CRM & RM