Bounded Rationality

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Welcome
Yield Management Jonathan Wareham j.wareham_at_esade
.edu
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Fixed Prices
P
1.00
1 Coke
Q
3
Fixed Prices
Consumers Surplus
Dead Weight Loss
MC
4
Get a little more revenue
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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

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3rd Degree Price Discrimination

• The practice of charging different groups of
  consumers different prices for the same product
• Examples include student discounts, senior
  citizens discounts, regional international
  pricing, coupons

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

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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 wont work if you cannot
  control three things
• Preference profiles
• Personalized billing (anonymous transactions
  lesson sellers discriminatory power over
  consumers)
• Consumer arbitrage

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How Many Versions?

• One is too few
• Ten is (probably) too many
• Two things to do
• Analyze market
• Analyze product

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Goldilocks Pricing

• Mass market software (word, spreadsheets)
• Network effects
• User confusion
• Default choice 3 versions
• Extremeness aversion
• Small/large v. small/large/jumbo

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Extremes Aversion

• Bargain basement at 109, midrange at 179
• Midrange chosen 45 of time
• High-end at 199 added
• Mid-range chosen 60 of time
• Wines
• Second-lowest price
• Framing effects-example

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Cross-Subsidies

• Prices charged for one product are subsidized by
  the sale of another product
• May be profitable when there are significant
  demand complementarities effects
• Examples
• Browser and server software
• Drinks and meals at restaurants
• Long distance and local access
• Auto spare parts
• Razor Blades
• Burger, fries, drinks
• Auto financing

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Lessons

• Version your product
• Delay, interface, resolution, speed, etc.
• Add value to online information
• Use natural segments
• Otherwise use 3
• Control the browser, access, comparisons, etc.
• Bundling cross subsidies may reduce dispersion

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Down Dirty

• First degree (perfect) price discrimination
• market of one
• Second degree price discrimination
• product line pricing, market segmentation,
  versioning
• Third degree price discrimination
• different prices to different groups
• Other definitions in literature

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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 Intl 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
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RM Evolution
Telco/ISP
Cruise lines
Energy
Media
2000
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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

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Major Types

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

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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

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The RM Challenge
Arrivals of high paying customers Closer to
departure!
Arrivals of low paying customersEarlier!
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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)

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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

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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

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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

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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.

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Auctions

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

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The RM Challenge
Arrivals of high paying customers Closer to
departure!
Arrivals of low paying customersEarlier!
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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.

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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!!!!

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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

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Depiction What should Q be?
210 rooms
Q1 rooms protected (protection level)
Q
210- (Q-1) rooms sold at discount (booking limit)
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Decision Tree
Revenue
Yes sell (Q1) room now
105
Lower protection level from Q1 to Q?
Sold at full price later
159
No protect (Q1) rooms
Not sold by March 29
0
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Historical Demand
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Decision Tree
Revenue
Yes sell (Q1) room now
105
Lower protection level from Q1 to Q?
1-F(Q)
159
No protect (Q1) rooms
F(Q)
0
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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)lt105
• Or
• F(Q)gt(159-105)/159 0.339

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Rational

• Find smallest Q with a cumulative value greater
  than or equal to 0.339.
• Optimal protection is Q79 with a cumulative
  value of .341
• Booking limit 210 -79 131
• Save 79 rooms for business travlers
• Sell 131 rooms for tourist travlers

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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 SY tickets

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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) gt B/BC

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Overbooking Example

• of customers who book but fail to show up are
  normally distributed mean20 std.10
• It costs 300 to bump a customer
• Hotel looses 105 if it does not sell room at
  105
• Overbooking b/bc 105/(105300) .2592

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Overbooking Example

• From normal distribution we get
• F(-.65) 0.2578 F(-.64) 0.2611
• Take z-0.645
• Overbook Y20-(0.64510)13.5
• Excel Norminv(.2592, 20, 10) gives 13.5
• Round up to 14 means 21014224

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Overbooking metrics

• Service level based
• P(denial) 0.05
• Edenials2
• 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)

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Industries

• Overbooking
• Airlines
• Hotels
• Car rentals
• Education
• Manufacturing
• Media

• No Overbooking
• Restos
• Movies, shows
• Events
• Resort hotels
• Cruise lines

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• CRM
• Attract retain customers
• maximize profit from each customer
• Segment by customer LTV
• Price/availability fct. of forecasted customer
  LTV to the organization
• Ignores capacity issues and opportunity costs
  (displacement)
• Wealth of data

• DPRM
• generate revenue
• maximize profit from available assets
• Segment by customer WTP
• Price/availability fct. of forecasted demand
  available supply
• Ignores customer value issues and long term
  revenues
• Quantifiable value

Maximize long-term profits
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CRM RM
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Variables to track

• Actual win or loss
• Number of days played
• Credit history
• Length of stay at hotel
• Individual spending preferences
• Demographics
• Psychographic profiles

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Theoretical Revenue

• Theoretical
• (total amount wagered) X
• (house advantage)
• 100 hand x 10 hours x 100 Hands/hour x .01
  (house adv. 49/51) 1,000

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Can you track every single person???

• Not always
• Difficult in table games
• Theoretical
• (total amount wagered) X
• (house advantage)
• Where..
• Total amount wagered estimated average bet x
  estimated time played

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Future estimates

• ADT Average Daily Theoretical Revenue
• Assumes that this level is constant
• Multiply by estimated of days of future trip
  to gain value
• Combined with CRM data on consumption of food and
  beverage, entertainment, pshychographics, etc

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Rooms, a scarce resource

• Heads in beds make money on gaming
• Comp. Rooms traditionally a fixed number of
  rooms given to big gamblers
• Used averages to cost out, did not dynamically
  look at opportunity cost

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ReInvestment amount

• of the ADT
• ADT 1,000
• Reinvestment amount 30
• 300
• Total value of the room, FB, Entertainment, etc.
  must be less than the
• Room 200, FB 100, Ent. 80..more than ADT x
  reinvest.
• Ergotry and sell room..
• Sophisticated applications use dynamic pricing to
  asses opportunity costs..

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Requirements

• RM Yield management like the airlines..
• Player tracking systems..Use cards like Harras,
  to register all activity and psychographic
  profiles
• POS resturants, theaters, spas, retail stores,
  entertainment, etc
• CRM integrates all of the above!!
• Statistical analysis and optimization
  applications.