OPSM 405 Service Management Koç University OPSM 405 Service Management Class 15: Yield management: introduction Zeynep Aksin zaksin@ku.edu.tr

Fundamental Problem: Customer Demand Service Delivery System Variable Usage Limited Capacity Services cannot be produced in advance and stored for later consumption; they must be produced at the time of consumption.

Matching supply and demand in services Management options reject demand inventory excess demand (queueing) modulate capacity (add facililities, scheduling, resource allocation) modulate demand (pricing, yield management) Primary considerations return on assets operating costs revenue losses (opportunity costs) service levels

Successful implementations American Airlines $1.4B additional revenue over three-year period “Selling the right capacity to the right customer at the right price” Hertz 1-5% revenue increase annually ($10-50M per year) Marriott Hotels $25-35M additional revenue in 1991 Royal Caribbean Cruise Line $20M+ additional revenue per year Source: Arthur D. Little, 1992

When is this strategy appropriate? Limited flexibility in supply Variable/uncertain demand Price flexibility/segmentation possible Available data Examples: airlines hotels/resorts/theme parks car/equipment rental cruise ships freight shipping theater/performing arts broadcasting (TV, radio,etc.) utilities (elec., telecom) 1970s 1980s 1990s

Yield Management System Reservation System current demand cancellations Forecasting cancellation rate estimates future demand estimates Overbooking Levels overbooking levels Discount Allocation fare class allocations

What is revenue/yield management? Two Perspectives: 1) A Market Segmentation Strategy (capture consumer surplus) p q p0 p2 p1 Create separate “fare products” Intelligently allocate fixed capacity to products NOTE: Segmentation may make sense even with static allocation! Segmentation can also provide value (e.g. cancellation option)

Segmentation/product design Ideally, we would like to discriminate (sort) customers based on their actual willingness-to-pay (reservation price). Ex: Cust. Res. Price C1 $120 C2 $180 C3 $167 C4 $230 C5 $ 45 ===== $742 Consumer Surplus = $742 But willingness-to-pay is usually unobservable!

So we try to find a variable that is correlated with willingness-to-pay (a “sorting mechanism”) Cust. Res. Price Adv. Purchase? C1 $120 YES C2 $180 NO C3 $167 YES C4 $230 NO C5 $ 45 YES Create two produce (advance/late purchase) with two prices: Adv: $100 Late: $150 Consumer Surplus = $500

Sorting mechanisms Time of purchase/usage advanced/spot purchase day-of-week/season Purchase restrictions cancellation options minimum term Saturday night stay Purchase volume (individual vs. group) Duration of usage (single night/weekly rate) Customer affiliation corporate contract user Finding a good sorting mechanism is an art and requires a certain amount of trial and error.

2) Matching Price to Demand (peak-load pricing) High Low Discount xx xxxxxxxxxx Price Full Fare xxxxxxxx x Allocate more capacity to low price points if demand is weak; allocate more capacity to high price points if demand is strong Create a small number of “price points”

Example: Using capacity controls for peak load pricing Capacity = 100 seats Off-Peak Day Peak Day Demand Rev. Demand Rev. $50 fare 30 $1,500 150 $5,000 $25 fare 80 $2,000 20 $500 $75 fare 2 $150 80 $6,000 Single Price Two Prices $2,150 $6,500

Example 2 Flights Capacity = 3 seats Ex: $800 5 customers with different valuations NOTE: We usually cannot observe these valuations in practice 8:00 AM 1:00 PM $700 $400 $300 $200

$700 $700 Best single price: $700 Revenue: 2 x $700 = $1400 Maximum obtainable revenue $800 + $700 + $400 + $300 +$ 200 = $2400 Only 58% of maximum achieved! $800 $700 priced out $300 $400 $200

Discrimination via a “sorting mechanism” $800 Customers returning by Saturday A trait that is correlated with willingness to pay allows for discrimination - Saturday night stay - Advance purchase req. - Distribution channel (e.g. internet) $700 $400 Customers staying a Saturday $300 $200

$700 $400 $700 $400 Price discrimination: SA stay: $400 No SA stay: $700 Revenue: 2 x $700 + 1 x $400 = $1800 Maximum revenue $2400 Now 75% of maximum achieved! $800 $700 $400 priced out $300 $200

Implement dynamic pricing Capacity-controlled fares can be used to dynamically adjust the “effective price” of each departure. 8:00 AM 1:00 PM $700 $700 $800 $400 $700 $400 $400 Priced out We would like to price the empty flight to attract more traffic! How? $200 $300

X Capacity-controlled deep discount $700 $700 $800 $400 $700 $400 $300 8:00 AM 1:00 PM $700 $700 $800 $400 $700 $400 $300 $400 X No seats available $200 $200 $200 2 seats available Revenue = 2 x $700 + 1 x $400 + 2 x $200 = $2200 92% of maximum!

Example summary: 2 Flights 3 Seats each 1) Single price $1400 (+0%) 2) Two prices w/ sorting mechanism $1800 (+29%) 3) Two prices w/ sorting mech. & capacity-controlled deep discount $2200 (+57%)

Forecasting demand Data requirements Forecasting issues high-level of detail (origin-destination, fare-class, day-of-week, departure time) quantities tracked demand for each rate category/fare-class/departure cancellation rates no-show rates/ go-show rates daily processing Forecasting issues seasonalities trends special events Good forecasting and accurate data are essential

Announcement Midterm exam on Wednesday March 26 Will start at 12:30 sharp and end at 13:59 All topics until revenue management