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Revenue Management Presentation Scheme
1.Introduction2.Techniques3.Industry Applications
Introduction Techniques Industry Applications
Introduction To RM
● Definition● Origins● Pricing Segmentation
● Customer Segmentation● Product Segmentation
Introduction Techniques Industry Applications
Revenue Management Origins
● Airline Industry● Airline deregulation Act 1978● Low-cost and charter airline competition● Yield Management – Robert Crandall (American
Airlines) ● Revenue Management – Robert Cross (Delta
Airlines)
Introduction Techniques Industry Applications
Revenue Management Definition
“Application of information systems and pricing to allocate the right capacity to the right customer at the right place at the right time.” Kimes 1998“RM has been credited with improving revenues 3-7% in the airline, hotels and car rental industries” Queenan, Ferguson, Highie, Kappor 2007
Introduction Techniques Industry Applications
Seven RM Core concepts
● Consider price first when balancing supply and demand.
● Replace cost-based pricing with market-based pricing.● Sell to segmented micro-markets, not mass markets.● Save your products for your most valuable customers.● Make decisions based on knowledge, not supposition.● Exploit each products' value cycle.● Continually re-evaluate your revenue opportunities.
Cross 1997Introduction Techniques Industry Applications
Pricing Segmentation
Demand
Pric
e
DemandP
rice
ONE PRICE FOUR PRICES
Selling to Micro-markets
REVENUE GROWTH
Introduction Techniques Industry Applications
Pricing segmentation
A strategy aimed at differentiating customers in order to charge different prices to different customers based primarily on differences in willingness to pay. Two different non-exclusive strategies:●Customer Segmentation : offering different customers exactly the same product at different prices.●Product Segmentation: offering slightly different products targeted at different customer segments.
Introduction Techniques Industry Applications
Pure Customer Segmentation
● Difficult to implement● Unpopular with consumers● Sometimes illegalExceptions:
● student and senior citizen discounts● Group membership discount● Geographical (zone pricing)● couponing
Introduction Techniques Industry Applications
Product Segmentation● Inferior product to be sold to price-sensitive customers
● Es. restricted early booking, branded gasoline versus generic, national brand label versus store label versus generic.
● Geographical● Es. zone pricing, international pricing,
neighbourhood pricing for services● Time-based segmentation
● es. time of purchase(airline,hotels), time of use (fashion,usage-time), delivery time
Introduction Techniques Industry Applications
Buying Behaviour Matrix
Attribute Prices Sensitive Price Insensitive Characteristics CharacteristicsProduct Features Limited Comprehensive Physical Ordinary Luxurious Service Adequate Abundant Warranty Minimal Extensive
Perception Brand Preference Indifferent Loyal Status Unconcerned Conscious Reliability Inconsequential Critical
Circumstances Urgency Casual need Acute need Alternatives Multiple Few Purchase Size Large Small
Introduction Techniques Industry Applications
Common segment basesTime of purchase
Time of reservation
Day of the week
Cancellation likelihood
Senior and youth
Options and premptability
Channel
Trip length or length of stay
Saturday night stay
Group discounts
Package
Business and individuals
Size of business
Spend amount
Loyalty
Frequency
Delivery time
Introduction Techniques Industry Applications
Stepwise RM Approach
Segment the market
Predict Customer Demand
Optimize price
Recalibrate Dynamically
Segmentationbased on buying behaviour, not justcurrent and past classifications
Forecast ofdemand and capacity atproduct/pricelevel
Mathematicallydetermine capacityavailability andprice that maximizesexpected profit
Continuallymonitor performance and update market response
Cross 2009
Introduction References● 6th Annual Revenue Management & Price Optimization Conference,
Workshop “Principles of Revenue Management and Price Optimization” Robert G. Cross, Atlanta 2010
● 6th Annual Revenue Management & Price Optimization Conference, Workshop “Introduction to the science of Revenue Management an Price Analytics” Mark Ferguson, Atlanta 2010
● “Revenue Management” Robert Cross, Broadway Books NY 1997 ● “Theory and practice of Revenue Management” Talluri & Van Ryzin Springer
2004● “Restaurant Revenue Management”, Kimes etall Cornell Hotel and
Administration Quarterly June 1998 p.32-39● “A comparison of Unconstraining Methods to Improve Revenue
Management Systems” Queenan et all , Production and Operation management, Vol16 No6 pp. 729-746 November December 2007
Introduction Techniques Industry Applications
Revenue Management Techniques
● Capacity-based RM● Price-based RM● Common elements:
● Customer behaviour and market-response models● Economics of RM● Estimation and forecasting
Introduction Techniques Industry Applications
Capacity-based RM
HIGH VALUECUSTOMERS
LOW VALUECUSTOMERS
CAPACITY OVERBOOKING
TIME
BOOKING LIMIT
●Single Resource Capacity Control●Network Capacity Control
Introduction Techniques Industry Applications
Single Resource Capacity Control● n classes● Assigning booking limit for each class● Mutually exclusive segments
(customers in each segment can only afford one class)● Es. different classes of single flight leg
of hotel rooms for a given date
CLASS 1 CLASS 3
1012 8
CLASS 2
1012 8$100 $75 $50
Introduction Techniques Industry Applications
Introduction Techniques Industry Applications
Capacity-based RM
Algorithms
Static or Dynamic Discrete
or ContinuousOptimizationOr Heuristics
Arrival assumptionStatic: lower value consumers comes firstDynamic: arbitrary arrival order
Demand and capacity profileDiscrete: probability distribution increases “in jumps” Continuous: probability distribution increases continuously
Resolution approachOptimization: maximizes or minimizes a revenue functionHeuristics: solve a problem by the application of interaction rule cycles.
Adaptive or regular
UpdatingAdaptive: updates booking policy parametersRegular: no up-dating
Types of control
Booking Limit
Protection Level
Bid price
amount of capacity that can be sold to any particular class j at a given point of time (b
j)
specifies an amount of capacity to reserve (protect) for a particular class j or set of classes (y
j)
Threshold prices are defined in a bib price table (based on the remaining capacity or time). A request is accepted just if the revenue exceed the threshold price ¶(x), x = remaining capacity
Introduction Techniques Industry Applications
Control classification
Partitioned
Nested
bj = y
j , j = 1, ..., n
divides the available capacity into separate blocks (buckets) that can be sold only to the designed class.
the capacity available to different classes overlaps in a hierarchical manner with higher-ranked classes having access to all capacity reserved to lower ranked classes
bj = C - y
j, j = 2,...,n
C = capacityx=amount of capacity availablej=classes indexb=booking limity=protection level¶=bid price
Introduction Techniques Industry Applications
CLASS 1 CLASS 31012 8
CLASS 21012 8
$100 $75 $50b
1 = 30
y3 = 30
b3 = 8
b2 = 18y
1 = 12
y2 = 22
12 22 30x
$100
$75
$50
¶(x)
C = capacity x=amount of capacity available j=classes indexb=booking limit y=protection level ¶=bid price
Or opportunity cost is the expected loss in the future revenue from using the capacity now rather than reserving it to future use.
Value function V(x) measures the optimal expected revenue as a function of remaining capacity.
Displacement cost
Introduction Techniques Industry Applications
Single resource capacity Models
Introduction Techniques Industry Applications
Optimization models
DynamicProgramming (Brumelle & McGill 1993)Montecarlo method(Robinson 1995)
HeuristicsEMSR-A EMSR-B + Buy factor(Belobaba 1987, 1992)
Robbins-MonroAlgorithm(Van Ryzin&McGill 2000)
Adaptive methods
Network Capacity Control● Products are sold as bundles● A lack of availability of any one resource in the bundle
limits sales● Es. ODIF (origin-destination itinerary fare class)
combination problem, room capacity on consecutive days when the customer stays multiple nights
Introduction Techniques Industry Applications
03/04 04/04 05/04 06/04
3 night stay2 night stay
1 night stay
CDG
FRA
MXP
GIG
FJK
Airlinehub
Network capacity types of control
PartitionedBooking Limit
Virtual Nesting
Bid-price
● allocate a fixed amount of capacity on each resource for every product that is offered. ● provide bounds on optimal network revenue● uses single-resource nested-allocation controls for each resource of the network. Indexing assigns products to virtual classes● sets a threshold price for each resource in the network.● a revenue request is compared with the sum of the bid prices of the resources required by the product
Introduction Techniques Industry Applications
Airline and hospitalitylegacy
Current practice
Network-based capacity Models
Introduction Techniques Industry Applications
Expected values/Demand distributionfor single itineraries
Linear and non-linear programming
Most used Heuristics: EMSR-B
Single CapacityProblemDecomposition
Simulation withStochastic Gradient
Demand sample generation
Overbooking● Increasing capacity utilization in a reservation based
system when there are significant cancellations(airline cancellations and no-shows: 50%) Smith et all 1992
● Customer relation policies: denied-services compensation strategies, selection criteria and oversale auctions Simon 1993
● Legal and regulatory issues ● Most common application : Airlines, Hotels, Car
Rental, Restaurants● New application: Sporting Venues, Manufacturing,
Professional ServicesIntroduction Techniques Industry Applications
Overbooking costs
Introduction Techniques Industry Applications
Underage cost
Industry Applications
Overage cost
Compensation: often free future ticket or stayProvision Cost: meals, drinks, giftsReaccomodation: sometimes list price at competitor )Goodwill: ( ways to reduce this? )
Airline: lost fare (but which fare?)Hotel/Casino: lost room rate + incidental profits
Number of overbooking
Overage cost
Underage cost
Total cost
Cost
Overbooking over time
Introduction Techniques Industry ApplicationsIndustry Applications
Time
Reservationswith Overbooking
Reservations without Overbooking
Overbooking limitReservations
Showdemand
T
C
Overbooking Models
Introduction Techniques Industry Applications
Staticmodels
Beckmann 1958,Taylor 1962,Thompson 1961,Rothstein&Stone 1967,Bierman & Thomas 1975,Martinez & Sanchez 1970
OverbookingCriteria
Show demand Distribution approximation
Service level
Ecomomic
Binomial
Deterministic
Normal
Gram-Charlier (Taylor 1962)
Customer Class Mix
Group Cancellations
Overbooking Models
Introduction Techniques Industry Applications
Dynamic Models(Chatwin 1999)
Exact approach
CombinedCapacity Control(Subramanian et all1999)
Substitutable Capacity(Karaesmen and Van Ryzin 2001)
Network Overbooking(Karaesmen and Van Ryzin 2001)
Heuristic approach
No-shows
Cancellations
Exact method
Class-dependent refunds
Dynamic Pricing● Elasticity of Price● Price versus quantity-based RM● Applications:
● Retail markdown pricing● Manufacturing● E-business
● Promotion Optimization● Auctions
Introduction Techniques Industry Applications
Price Response Function
Introduction Techniques Industry Applications
Demand
Price
Slope of tangent line = d´(p)
The slope, d'(p) measures the local rate of change of the price response function
p
Unit sensitive
Elasticity of priceε = percentage change in demand for a 1%change in priceε < 1 (inelastic): raising price will increase revenue (price insensitive)
ε = 1: Revenue is independent of price
ε > 1: Raising price will decrease revenue (price sensitive)
Depends on ● time period of measurement● level of measurement (industry elasticity may be low,
Individual product elasticity is always higher)
Introduction Techniques Industry Applications
Unit free
Price RM vs Quantity RM
Introduction Techniques Industry Applications
Price-based RM
Classic Retail applications
Quantity based RM
Classic Airlines, hotels, cruise ships and rent-a-car applications
Ability to vary price/quantity:●Firms commitments●Cost of making price changes●Flexibility in supplying products or services (re-order stock or reallocate inventory)(Galego&Van Ryzin 1997)
Elasticity of price
Introduction Techniques Industry Applications
Toilet tissue 0.6
Shampoo 0.84
Cake Mix 0.21
Cat food 0.49
Frozen entrèe 0.60
Gelatin 0.97
Soups 1.05
Automobiles 1.2
Chevrolets 4.0
Chevy Caprice 6.1
More specific means higher elasticity
Retailing Markdown pricing
Introduction Techniques Industry Applications
Style-GoodsMarkdown
pricing
Consumer Package-goods
Promotion
5%-15% gross margins improvement by the usage of model-based pricing software (Friend &Walker 2001)
●Apparel, sporting goods, high-tech and perishable foods●High initial price, mark-down low reservation items●Markdown on peak periods: more sensitive customer behaviour● Lazear 1986 Pashigan 1988 Warner &Birsky 1995 ●Soap, diapers, coffee, yogurt,...●Customer awareness of past prices/Promotions●“reference price”●“stockpile” behaviour
Retailing Markdown prospective
Introduction Techniques Industry Applications
Manufacture Retail
●Trade promotions:manufacture discounts to retailers (with or without retailer pass-thru)●Increase sales or profit for its brand●Kopalle et all1999, Silva-Russo et all 1999, Tellis & Zufryden 1995
●Retailer /consumer promotions: retail discount to customers●Overall sales of profits for a category of multiple brands and multiple manufacturers products. ●Incentive compatibility constraints●Greenleaf 1995, Kopalle et all 1996
Discount Airline pricing
Introduction Techniques Industry Applications
●Price change in a limit set of values: 1.99, 9.99, 19.99●Price rises over time depending on capacity and demand for a specific departure
Week prior0 1 2 3 4 5 6 7 8 9 10 11 12
Fare250
150
100
50
200
Dynamic Pricing Algorithms
Introduction Techniques Industry Applications
Function ofDemand variationover price and time
Mathematical Programming
Probability Distribution of Demand variationover price and time
Dynamic StochasticProgramming
Techniques References● “Theory and practice of Revenue Management” Talluri & Van
Ryzin Springer 2004● “Algoritmi per il Single Resource Capacity Problem” “Algoritimi
per il Network Resource Capacity Problem” “Price-Based Revenue management”, Fabio Colombo, DTI-UNIMI
● 6th Annual Revenue Management & Price Optimization Conference, Workshop “Introduction to the science of Revenue Management an Price Analytics” Mark Ferguson, Atlanta 2010
Introduction Techniques Industry Applications
Industry Applications
Introduction Techniques Industry Applications
● Airlines● Hotels● Restaurants● Retailing● Manufacturing
Tipology of Revenue Mangement
Introduction Techniques Industry Applications
Fixed Variable
Predictable
Unpredictable
Duration
Price
MoviesStadiums and
arenasConvention centers
HotelsAirlines
Rental carsCruise lines
RestaurantsGolf courses
Internet service providers
Continuing careHospitals
Kimmes1998
Hotels
Introduction Techniques Industry Applications
Room occupancy
Out-housefood and beverage
Applied techniques:●Overbooking (Hadjinicola&Panayi 1997)●Traditional nested allocation and bid price●minimum/ maximum length of stay control●intradays/hour forecast updates and optimization
Events,conventionsconferences
Out-housefood and beverage
Property Management
Systems (PMS)
GDSReservations
ControlsJust 20%
Hanks, Cross Noland 1992Kimes1989Orkin1988Varini et al 2002Burns 200Bitran and Modschein 1995
Airlines
Introduction Techniques Industry Applications
Airline host
reservation systems
GDSReservationsControls
(AllocationOverbooking) Sales/
CRM
Callcenter
Webserver
Applied techniques:●Overbooking●Fare classes●Public fare x private fares●Internet-only fares●Capacity controls by availability fare classes posted on GDS
Barnhart Talluri 1997
Manufacturing
Introduction Techniques Industry Applications
Few current applications of RM (Gray 1994).SCM and ERP vendors are starting to offer pricing optimization systems.
Service Manufacturing
Perishable inventory Inventory can be stored for future use
Order has to be denied Order can be delayed
RM Applications opportunities● Perishable products like high-tech products and concrete. (Kalyan 2002, Elimam &Dodin 2001)● Demand smoothing: delaying production of low-value demand to off-peak times, while ensuring prompt production of high-value demand during peak times.
Restaurants
Introduction Techniques Industry Applications
Price
Meal duration
Maximize Revenue per available seat hour (RevPASH)
Possible research area: “Menu engineering”Main author: Sheryl E. Kimes - Cornell University
Revenue drivers
Retailing
Introduction Techniques Industry Applications
POS
ERP/SCM
Retail RM systems functions:●Price Optimization●Automate routine price changes by location and channel●Monitoring profit and sales targets for items and categories●Tracking performance of promotions and advertising campaigns●Maintaining consistent pricing and rounding rules●Automating price matching based on competitors prices●Supporting price-sensitivity experiments●Generating reports ans statistics to track pricing performances
Store's historical data
Retail RMsystem
Dynamic price bottleneck
Menu costs(Cost on changing prices)
ESLs (electronic shelf labels)not widely deployed
Girad 2000Johnson 2001Mantrala&Rao 2001
Applications References● “Theory and practice of Revenue Management” Talluri & Van
Ryzin Springer 2004● 6th Annual Revenue Management & Price Optimization
Conference, “A Tag Approach to Effective Communication” Randy Light and Bill Dudziak, The Home Depot, Atlanta 2010
Introduction Techniques Industry Applications