welcome yield management jonathan wareham j.wareham@esade.edu

Welcome

Yield Management

Jonathan Warehamj.wareham@esade.edu

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