Revenue Management - unimi.itusers.mat.unimi.it/users/novaes/RM_v3.pdf · 6th Annual Revenue Management & Price Optimization Conference, ... Robbins-Monro Algorithm (Van Ryzin&McGill

Revenue Management

Adriana NovaesJanuary 2011

Revenue Management Presentation Scheme

1.Introduction2.Techniques3.Industry Applications

Introduction Techniques Industry Applications

Introduction To RM

● Definition● Origins● Pricing Segmentation

● Customer Segmentation● Product Segmentation


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)


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


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

Common pricing practice

UnitaryCost Margin

Price

Discounts

Pricing Segmentation

Demand

Pric

e

DemandP

rice

ONE PRICE FOUR PRICES

Selling to Micro-markets

REVENUE GROWTH


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.


Pure Customer Segmentation

● Difficult to implement● Unpopular with consumers● Sometimes illegalExceptions:

● student and senior citizen discounts● Group membership discount● Geographical (zone pricing)● couponing


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


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


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


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


Revenue Management Techniques

● Capacity-based RM● Price-based RM● Common elements:

● Customer behaviour and market-response models● Economics of RM● Estimation and forecasting


Capacity-based RM

HIGH VALUECUSTOMERS

LOW VALUECUSTOMERS

CAPACITY OVERBOOKING

TIME

BOOKING LIMIT

●Single Resource Capacity Control●Network Capacity Control


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



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


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


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


Single resource capacity Models


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


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


Airline and hospitalitylegacy

Current practice

Network-based capacity Models


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


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


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


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


Price Response Function


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)


Unit free

Price RM vs Quantity RM


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


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


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


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


●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


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


Industry Applications


● Airlines● Hotels● Restaurants● Retailing● Manufacturing

Tipology of Revenue Mangement


Fixed Variable

Predictable

Unpredictable

Duration

Price

MoviesStadiums and

arenasConvention centers

HotelsAirlines

Rental carsCruise lines

RestaurantsGolf courses

Internet service providers

Continuing careHospitals

Kimmes1998

Hotels


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


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


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


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


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


Revenue Management - unimi.itusers.mat.unimi.it/users/novaes/RM_v3.pdf · 6th Annual Revenue Management & Price Optimization Conference, ... Robbins-Monro Algorithm (Van Ryzin&McGill

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