retail po at ihg

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Vol. 42, No. 1, JanuaryFebruary 2012, pp. 4557ISSN 0092-2102 (print) ISSN 1526-551X (online) http://dx.doi.org/10.1287/inte.1110.0620

2012 INFORMS

THE FRANZ EDELMAN AWARD

Achievement in Operations Research

Retail Price Optimization at InterContinentalHotels Group

Dev KoushikIntercontinental Hotels Group, Atlanta, Georgia 30346, [email protected]

Jon A. HigbieRevenue Analytics, Atlanta, Georgia 30339, [email protected]

Craig EisterIntercontinental Hotels Group, Atlanta, Georgia 30346, [email protected]

PERFORMSM with price optimization is the first large-scale enterprise implementation of price optimization inthe hospitality industry. The price optimization module determines optimal room rates based on occupancy,price elasticity, and competitive prices. The approach used is a major advancement over existing revenue man-agement systems, which assume that demands by rate segments are independent of price and of each other.As of this writing, over 2,000 InterContinental Hotels Group (IHG) hotels use the price optimization module;all IHG properties will eventually use it. To date, price optimization has achieved $145 million in incrementalrevenue for IHG. At full rollout, we anticipate that this capability will generate approximately $400 million

per year.Key words : hotel pricing; price optimization; revenue management; price elasticity; competitor rates.

InterContinental Hotels Group (IHG) is the worldslargest hotel group based on number of rooms.Through its various subsidiaries, IHG owns, man-

ages, leases, or franchises over 4,500 hotels and more

than 650,000 guest rooms in nearly 100 countries

and territories worldwide. It owns a portfolio of

well-recognized and respected hotel brands, including

InterContinental Hotels, Hotel Indigo, Crowne PlazaHotels and Resorts, Holiday Inn Hotels and Resorts,

Holiday Inn Express, Staybridge Suites, and Can-

dlewood Suites. It also manages the worlds largest

hotel loyalty program, Priority Club Rewards, which

has 52 million members worldwide. Approximately

85 percent of IHGs hotels are franchised, 14 percent

are managed, and 1 percent are owned.

Each hotel, including corporate-owned and man-

aged properties, is responsible for its own profit and

loss, essentially operating as an independent busi-

ness. Each hotels revenue manager is responsible

for optimizing that hotels revenue performance by

undertaking key revenue strategies with respect to

pricing and inventory management. They include

demand forecasting, inventory control management(overbooking and length-of-stay (LOS) controls), price

execution (rate implementation and adjustments), and

collaboration with the hotels general manager on

strategy and business planning. Some hotels have an

on-site dedicated revenue manager, titled a director of

revenue management (DORM); other hotels are part

of a corporate revenue management services group,

45


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Koushik, Higbie, and Eister: Retail Price Optimization at IHG46 Interfaces 42(1), pp. 4557, 2012 INFORMS

which manages pricing and inventory on behalf of the

hotels and is generally located remotely.

A corporate revenue management (RM) team sup-

ports the hotel revenue managers by providing sys-tems, strategy, and a support organization of regional

DORMs and geographic divisional vice presidents

(e.g., for North America and the Asia Pacific regions).

IHGs globally distributed RM organizational struc-

ture is the norm for the hotel industry; however, it dif-

fers from the highly centralized RM structure in the

airline industry. The complex organizational struc-

ture of hotel RM presents significant challenges with

respect to training, adoption, and consistent execution

of RM strategies and system use.

The application of RM in the hotel industry was

adapted from airline industry RM systems, whichthe industry began to implement in the 1980s (Cross

et al. 2009). Since their inception, hotel RM systems

have opened and closed rate products, the prices of

which are predetermined via manual processes, with-

out analytics. These systems assume that demand

by rate segment is independent, an assumption that

is not true and can lead to a downward spiral in

rates when demand is soft (Cooper et al. 2006). This

approach is similar to that of the early airline models

(Smith et al. 1992), which open and close fare classes

under the assumption that demands by fare class are

independent.PERFORMSM is a Web portal through which more

than 4,000 users worldwide access IHGs RM sys-

tem and related tools. Like the RM systems at other

major hotel enterprises and prior to implementing

price optimization, PERFORM optimized availability

and LOS inventory controls based on the assump-

tion of independent demand. The deterministic model

described by Baker and Collier (1999) is the most com-

mon formulation used in practice. Like that used by

some other hotel RM systems, the PERFORM yield

management optimization model was a variant of the

deterministic model with stochastic demand.

The growth of Internet booking channels starting

about 2000, the deepening travel recession starting

about 2001, and the tragic events of September 11, 2001

combined to drive hotel RM systems to incorporate

pricing as well as inventory yield techniques (Cross

et al. 2009). Hotel occupancy rates fell by 1520 per-

cent at leading hotel groups (Cross et al. 2009), and US

hotel profits fell by $642 million (Bowers and Freitag

2003). Soft demand for hotel rooms also lessened the

benefits of the PERFORM system because the ben-

efits of yield management models primarily derivefrom tightening inventory controls when demand is

strong. Internet booking channels created increasing

price transparency, allowing consumers to compari-

son shop multiple hotels to find the best deal. Price

transparency and the need to drive demand con-

tributed to the erosion of rate fences (restrictions),

which are essentially qualifications on bookings that

support segmented demand and pricing. The erosion

of rate fences undermined the RM assumption of

independent demand.

The hotel industry has traditionally divided de-

mand into two broad segments: group and transient.The group segment includes conferences and corpo-

rate events for which a hotel contracts with a group

to commit large blocks of rooms for a specific period.

The transient segment represents all individual book-

ings. The objective of hotel RM systems is to opti-

mize revenues for the transient segment. Although

many RM systems also include a group yield mod-

ule, transient and group segments are managed sepa-

rately. Lee et al. (2011) divided the transient demand

into retail and negotiated segments. Negotiated seg-

ments include corporate special rates for large cus-

tomers (e.g., IBM and HP). These rates are typicallyfixed and are not subject to dynamic price changes.

Most also have last-room availability clauses; thus,

they are not subject to the inventory controls that RM

systems generate. Only the retail segment is subject

to the full range of pricing and inventory controls.

Lee et al. (2011) further segmented retail demand into

restricted and unrestricted segments. In a study of

20062007 hotel demand, these authors demonstrated

that rates paid by unrestricted retail customers do not

tend to increase as the day of arrival approaches and

that restricted rates actually tend to decrease. This

observation is contrary to the long-held belief that cus-

tomer willingness to pay increases as the day of arrival

approaches. A cornerstone of yield management and

RM systems is the assumption that higher-booking

customers book late in the booking cycle. Lee et al.

(2011) assertand the authors of this paper agree

that segmenting hotel demand into group and retail

segments better aligns with how consumers view


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Koushik, Higbie, and Eister: Retail Price Optimization at IHGInterfaces 42(1), pp. 4557, 2012 INFORMS 47

hotel products and that the studys findings seriously

challenge the assumption of independent demand byrate segment and the assumption that willingness to

pay increases as the day of arrival approaches.The decline in hotel demand, the rise of Internet

booking channels and price transparency, and chal-

lenges to underlying RM assumptions drove changesin hotel RM workflow. IHG adapted to this changing

environment by revamping its pricing strategy, shift-ing the focus from an inventory allocation approachto a pricing focus. It implemented a rational pricing

structure within which restricted retail discount rateswere tied to the unrestricted best flexible rate (BFR).

A uniform rational rate structure facilitated dynamicpricing.

IHG, without an automated capability to opti-mize prices, undertook a process of educating itshotel staffs on the need to flex their BFRs based on

demand. Because of this strategic shift, IHG property-based DORMs were spending, on average, morethan 30 percent of their time gathering competitive

price intelligence. This intelligence included competi-tor rates, which they found on the Internet or through

third-party sources, including TravelClick and Rubi-cons MarketVision reports. DORMs changed rates

through IHGs central reservations system, HOLIDEXPlus. HOLIDEX Plus is a mainframe system, which

was not designed to facilitate frequent rate changes;its pricing mechanism is cumbersome and time con-suming. As a result, pricing analysis was ad hoc,

response to competitive actions slow, and executioninconsistent. Forecasting represented 30 percent of aDORMs time, much of it to adjust forecasts for rate

changes in the DORMs own property and in compet-itive rates. DORMs spent only 20 percent of their time

performing more strategic analysis and business plan-ning and less than 10 percent in managing inventorycontrols. The desired division of time among tasks

is 40 percent in forecasting, 40 percent in strategy,

10 percent in pricing, and 10 percent in inventory con-trol. Corporate RM realized that the DORMS neededsystem support for pricing (including price-adjusted

forecasting) to better use their time, to improve thequality of pricing decisions, and to facilitate improvedexecution of pricing best practices. A key part of the

solution was a price optimization capability that opti-mizes prices for the retail segment, considers com-

petitor rates, and generates a price-adjusted forecast

based on price sensitivity. A new price optimization

module in PERFORM would support pricing analysis,

recommend prices, adjust forecasts based on IHGs

own and competitive prices, and automate price exe-cution. New reporting and a new interface to execute

prices in HOLIDEX Plus would be essential.

Building the Business CaseDesigning and implementing the price optimization

capability was a major cross-functional effort. Corpo-

rate RM led the analysis and design, IHGs informa-

tion technology (IT) group developed the new screens

and reports, and a training group developed and

delivered a global training program. IHG corporate

and franchise hotel management needed to buy into

the changes. The executive leadership team needed to

approve the large corporate capital outlay and support

the massive change management process. However,

management had been burned many times by large

capital expenditures that failed to deliver promised

benefits; therefore, the executive team wanted quan-

tifiable proof that implementing the price optimization

capability would increase profits.

In the third quarter of 2006, IHG engaged Rev-

enue Analytics. The two organizations formed a part-

nership in which they jointly conducted a research

and scoping project that lasted through the design,

development, and deployment of the price optimiza-

tion module. The projects goals were to demonstrate

the feasibility of price optimization, develop an ini-

tial estimate of its potential benefits, and identify other

capabilities that needed to be upgraded to enable

price optimization. The project team developed a sim-

ulation model, which estimated the theoretical ben-

efits of price optimization to be from 2.75 percent

to 6 percent revenue uplift on the retail segment.

It also determined that it needed to upgrade the

existing RM forecast algorithms. Transient forecast

errors reduced the benefits of optimizing price by1.3 percent. Group forecast errors reduced benefits by

0.7 percent. Figure 1 shows the sensitivity of the price

optimization simulation to forecast error by elasticity.

Based on this analysis, the team immediately

launched projects to improve the PERFORM RM fore-

cast models.

Although the research and scoping phase required

extensive applications of analytics, a few simple


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Price optimization benefit average uplift for a sample of 776 properties

12.0

10.0

8.0

6.0

4.0

Revenueuplift(%)

2.0

0.0

2.0 0.8 0.9

Elasticity

1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0

Impact of group forecast error

Impact of group and transient forecast error

Price optimization benefit

Figure 1: Transient and group forecast errors significantly reduced the benefits of price optimization.

models were decisive in communicating optimiza-

tion concepts to the chief marketing officer (CMO),

brand presidents, and other senior executives. The

only way to accurately communicate the form of

the price optimization model is through mathematics,

which is not the preferred method of explanation for

most senior executives. We found that the simple two-

dimensional example depicted in Figure 2 was pow-

erful in explaining concepts to the executives whose

approval we needed to fund our price optimization

project.

To help us in gaining executive approval, we

decided to build a simple interactive simulation model

in the form of a game (see Figure 3) in which the audi-

ence would try to guess what the optimal price should

be. The base-case scenario formed a business-as-usual

point of reference. For each turn of the game, competi-

tor rates, demand, and capacity varied; the object was

to guess the rate that would optimize revenue. Thegame was fun, but also communicated the challenges

revenue managers faced in determining the best rate

for a single date. It reminded the audience that rev-

enue managers had to handle multiple-rate products

for 350 future arrival dates, while accounting for LOS

interactions. If senior executives could not guess the

right price in this simple game, how much more

challenging was the problem that the hotel revenue

managers faced? The pricing game was key in secur-

ing the funding we needed.

In the second quarter of 2007, we received fund-

ing approval for a high-level design and live market

test project. The market test required construction

of a working price optimization prototype, which

we would deploy to a limited number of hotels.

These hotels would use the prototype system to man-

age rates for their hotels for the duration of the

test. In addition to providing valuable feedback from

DORMs on the design, the market test would serve as

a robust measure of the achievable benefits. The exec-

utive team and capital committee demanded proof

from live market tests, not merely theoretical esti-

mates from a simulation. The IHG capital committee,

which includes the most senior IHG executivesthe

CEO, the CFO, the CMO, and at least one regional

president, is responsible for releasing funding for

large investments. The high-level design would pro-vide enough detail to enable the IT group to esti-

mate price optimization development costs. As part

of the high-level design, the combined IHG and RA

operations research (OR) teams would research and

prototype the four other price optimization models:

elasticity and price-sensitive demand forecast, LOS

price optimization model formulation and solution

algorithm, competitive rate shopping algorithm, and


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Contribution function

Room rate ($)

Contribution($)

Unconstrained contribution

Demand

30,000500

450

400

350

300

250

200

150

100

50

Demand(roomn

ights)

25,000

20,000

15,000

10,000

5,000

Constrained contribution

50 75 100 125 150 175 200

Figure 2: Demand is simply a linear function of price. In this example, the unconstrained optimal price is $110;however, because the hotel capacity is only 200 rooms, the optimal constrained price is $130.

Base case information

Benchmark rates

Holiday Inn (HI) Quality Inn Courtyard Comfort Inn Best Western Price elasticity

$97.47 $79.00 $119.00 $89.00 $99.00 1.3

The pricing game

Quality Inn CourtyardComfort

InnBest

WesternExpecteddemand

HIcapacity

Bestguess HI

rate

Bestguessroomssold

Optimal HIrate

Optimal HIroomssold

Guessrevenue

$79.00 $109.00 $89.00 $99.00 38 40 $99 36 $91.10 40 $3,552.86

k to clear Winner?Margin of

victory

Optimizer

a for answer 2.6%

Holiday Inn-Highway Location-Tuesday

Figure 3: The pricing game was one of the most compelling tools we used to communicate the need for priceoptimization to senior executives. We challenged the executives to guess the revenue optimal price under varyingsupply, demand, and competitive pricing conditions. In the game, we show the key input parameters and therecommended rates using a glass-box solution framework; next to the recommended price, we present key dataelements (e.g., occupancy, current IHG and competitor prices, IHG and competitor reference prices, and pricesensitivity) to validate the price recommendations.


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the competitive rate fill-in logic. To implement the

prototype and conduct the live market test, we had

to implement all these models, which we describe in

the Core OR Models section and the appendices.To expedite the prototype development, we

decided to simplify the optimization to a staynight

model, which essentially assumes that all demand is

for only one night. The production system would be

an LOS model, which recognizes that guests can stay

for multiple nights. Modeling multiple-night stays

requires a network structure for the constraint matrix

to account for contention of different LOS periods for

the same room on a given night.

Figure 4 shows the prototypes structure. We imple-

mented the prototype in Excel VBA, connected it to an

Oracle database (the prototype DB), and refreshed thisdatabase weekly from IHGs enterprise data ware-

house (EDW) and the PERFORM RM tables.

In July 2007, we deployed the first prototype to

a hotel; eventually, we deployed it to 18 properties.

We conducted the live market test on 13 properties

over a 16-week period. To account for reservations on

Competitive rates

Perform/RMEDW

Prototype DB

PO prototypeworksheets

Figure 4: The prototype consisted of seven user screens and three screensto allow an administrator to gather and report usage statistics.

the books and a ramp-up period, we excluded the first

four weeks of the pilot, leaving a 12-week test period.

For each of the 13 treatment properties, we selected

1 to 4 control properties (34 control properties in all).Variables controlled for included brand, region, prop-

erty size, and group mix. The 12 weeks prior to the

prototype roll-out were the baseline period. We con-

trolled for day of week by ensuring that the baseline

and test periods had equal numbers of each day of the

week. We assumed that seasonality was the same for

the prototype and control properties. Figure 5 illus-

trates the concepts of baseline and test periods and of

prototype and control properties.

The benefit metric we used was total revenue per

available room (REVPAR), which is the total revenue

divided by the number of room nights available forsale. Although the price optimization function only

recommended retail price changes, total REVPAR

includes group and negotiated segments for these

reasons. (1) REVPAR is the most important per-

formance metric because the executives and capital

committee understand it clearly. (2) We considered,

but rejected, transient REVPAR (transient revenue

divided by total rooms). Total REVPAR can vary

widely as the occupancy rate varies. REVPAR instabil-

ity is even more pronounced if we subdivide revenue

by group and transient segments. (3) Retail prices

indirectly influence group and negotiated rooms soldand rates; therefore, some price optimization bene-

fits are expected in these segments. Using a Pearsons

chi-squared test, we concluded with 99 percent con-

fidence that the prototype properties outperformed

their control properties during the test period (and

relative to the baseline period). The mean improve-

ment in REVPAR was 3.2 percent.

Anecdotal feedback on the price optimization pro-

totype was also positive. For example, in response

to our request for feedback, Brian Cauwels, revenue

manager for a Holiday Inn Express in Louisville, Ken-

tucky, reported We had the highest revenue week

ever, aside from the Derby weekend, using the rec-

ommended rates of the tool. The GM [general man-

ager] became a big believer in pushing rate after he

saw the revenues from the first night of the week.

Balazs Szentmary, revenue manager for the InterCon-

tinental Madrid, wrote Great Tool! [It] challenges

you to question your pricing practices. We collected


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Baseline period Test periodExcludedPrototype property

Baseline period Test periodControl property

Pilot launch

Time (weeks)

Figure 5: The test period was 12 weeks starting at week five following the prototype launch. The baseline periodcovered the 12 weeks prior to prototype launch.

detailed user feedback, incorporated it into the tool,

and performed additional analytics on usage statis-

tics. Figure 6 shows the utilization of the various

prototype screens. The staynight screen was the most

heavily used. This guided the design of the optimize

price screen in the production system (see Figure 9).

The calendar view was woven into the overall nav-

igation. The workbench screen proved important as

users gained familiarity with the system; however,

because the optimize price screen was so critical, we

had to add quick links into the production system to

allow the users to navigate directly to this screen.

The live market test provided a rigorous benefits

estimate for the new price optimization capability.

The prototype and the high-level design enabled usto reliably estimate the time and cost required to con-

struct the production system. Feedback from proto-

type users added weight to the quantitative benefits

Reservations8%

Bus. rules5% Calendar

7%

Workbench26%

Staynight32%

Comp. rates11%

Analysis11%

Figure 6: The chart shows the relative utilization of the prototype screens.This feedback from the prototype helped guide the design of the produc-tion system.

estimate of the prototype. The benefits of the proto-

type were so substantial that some DORMs pleaded

to keep the prototype running. As a result, we were

able to build a solid business case for a production

version of the price optimization capability. In the

fourth quarter of 2007, based on the detailed benefits

estimate, the support of hotel general managers, and

recommendations of the property and regional rev-

enue managers, the IHG capital committee approved

a multimillion dollar budget to develop the price opti-

mization module within PERFORM.

Production System Development,Deployment Plan, and Revenue Uplift

Estimates from Beta Release PropertiesDevelopment began in January 2008. The OR team

implemented the market response model (MRM),

competitive rate shopping module, the rate expan-

sion module, and the core price optimization engine.

IT implemented the data model, the server that inte-

grates all modules, the user interface, job scheduling,

and the configuration of new servers for the price

optimization capability. The RM strategy team and

the OR team developed and implemented the change

management plan and the rollout plan and worked

with the training group to develop training modules.

The MRM describes the relationship between

demand and other driver variables. The competitive

rate shopping module specifies which future arrival

dates and LOS products should be shopped. Shop-

ping all future combinations of arrival date and LOS

would overburden the global hotel distribution sys-

tem; thus, it is not feasible. Because only a sample of

future arrival dates and LOS periods are shopped, the


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Server

Competitiverates

EDW

Comp rates

MRM

Market visioncompetitor rates

Rate expansion

Rate shopping Shop requests

Optimizationengine

Figure 7: The flowchart shows the logical relationship between the rate-shopping module, the MRM module, theprice optimization engine, and the server in the price optimization architecture.

rate expansion module infers rates for products thatare not shopped. The price optimization engine builds

the optimization model formulation from the input

data and solves for optimal prices. Figure 7 depicts

the relationship between the core modules.

We used a decomposition approach to model

demand as a function of price; we modeled it as

independent of price and then modeled the remain-

ing variability in demand as a function of price. This

approach fit the data well and aligned with the deci-

sion to leverage existing PERFORM RM modules to

the fullest extent possible. In particular, we wanted

to continue to use the existing forecasting and yield

optimization functions.

There were six fundamental reasons for continu-

ing to use the existing PERFORM modules. (1) Rev-

enue managers were already effectively using much

of the PERFORM functionality, including user screens

and reports. (2) The existing forecast, although not

price sensitive, was reasonably accurate. (3) The LOS

recommendations that the yield optimization modulegenerated were good. (4) Pricing decisions and yield

decisions are made at different frequencies. Inventory

controls change constantly and in real time as book-

ings are made; however, hotels prefer to change prices

less frequently. (5) Implementing price optimization

would require extensive retraining of revenue man-

agers and careful configuration of each property,

necessitating a staged rollout. Therefore, the existing

PERFORM system would have to continue to function

for other properties during the rollout. (6) Leveraging

the existing modules would accelerate delivery of the

new system.

Price optimization works in conjunction with the

existing PERFORM forecasting and yield optimiza-

tion components. The MRM modifies the PERFORM

forecast at the optimal prices to make it price sen-

sitive. The price-neutral unconstrained demand fore-

cast, available capacity, and competitive rates are the

key inputs to the price optimization engine. Plugging


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CRSCentralized reservation system

Forecasting engine

Price optimization engine

Yield optimization engine

Price-sensitive forecast

Price-sensitiveforecast

CRS

Optimal prices

Optimal length of stayPrice-insensitive forecast

Figure 8: The forecasting engine generates a price-neutral demand forecast. The price optimization engine gen-erates optimal prices, and a price-sensitive forecast is computed at these prices based on the MRM. The yield

optimization engine leverages the price-sensitive forecast to generate LOS controls.

the optimized rates into the MRM produces the price-

sensitive demand forecast at the new rates. After

the rates have been updated, the yield optimization

engine uses the price-sensitive demand forecast to

update the LOS inventory controls at the new rates.

Figure 8 depicts how PERFORMs forecasting, yield

optimization, and price optimization engines work

together.

RM executives insisted that the solution not be a

black box. Presenting the critical components driv-ing the pricing recommendation with the recom-

mendations was critical. Data presented include the

three pillars of pricingcompetitive rates, forecasted

occupancy, and price-sensitivity ratings derived from

elasticity estimates. Figure 9 shows a screenshot of

PERFORMs optimize price tab, which is used to

review price recommendations and publish them to

the central reservation system. Training on the three

pillars, including a variation of the game we devel-

oped in the research and scoping phase, is a critical

prerequisite that revenue managers must meet before

using the price optimization functionality. This train-

ing and the need to carefully configure each prop-

ertys competitive setthe set of competitors whose

rate data need to be shopped (a critical input to price

optimization)are the two main reasons the price

optimization rollout had to be gradual.

Alpha testing for price optimization began in the

first quarter of 2009. Training of regional DORMs and

revenue managers at beta test properties began simul-

taneously. Beta testing in the production environment

also began, and continued until the third quarter of

2009. Starting in the fourth quarter of 2009, priceoptimization rollout began for the rest of the IHG

properties at a rate of approximately 100 per month.

In the third quarter of 2009, after beta properties had

been using the capability for several months, we con-ducted a benefits measurement study similar to that

conducted following the prototype market test. This

study showed a 2.7 percent increase in REVPAR for

the beta test properties. As of this writing, more than2,000 properties worldwide are running PERFORMs

price optimization, and we add about 100 proper-

ties each month. The global nature of this capability

means that some core OR models for shopping com-petitive rates must be modified to also account for

booking channels.

Core OR ModelsPrice optimization development involved intensive

OR modeling. Given the scope of this paper, wecannot describe all the work in detail; however, in

this section we will outline five key areas in which

we applied ORMRM, competitive rate shopping,

benefits estimation, rate expansion and fill-in logic,and optimization model. The IHG OR and Revenue

Analytics OR teams designed and implemented each

model and then integrated it with the server and user

interface, which IHG IT developed.


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Figure 9: The optimize price tab in PERFORM displays all the information needed to make the price recommen-dations transparent. The benchmark rate is an aggregation of competitor prices. By comparing the remainingcapacity, remaining demand, competitor rates, and our current BFR, revenue managers can intuitively judge thereasonableness of the price recommendations. As suggested by the other tabs, price optimization also providesa capability to drill down into demand forecasts, competitive rates, current bookings, and additional pricing

analysis.

Market Response Model (MRM)

The MRM describes demand as a function of price and

other driver variables. Because conducting real-time

price experiments to estimate price sensitivity is dif-

ficult in IHGs distributed environment, we decided

to use pseudo-random price experiments to mine his-

torical prices, historical demand, and historical com-

petitor rates to measure the response of the demand

changes against IHG and competitor price changes.

A key input from the hotel revenue managers was thatif they were to have greater acceptability of the price

optimization capability, modeling demand as a func-

tion of competitor rates would also be imperative.

Within the MRM module, we modeled demand

as a function of price and competitor rates to com-

pute price elasticity. We tried various segmentation

schemes and decided on a segmentation approach

that aligned with key business segments at which

elasticity estimates were significant and that the rev-

enue managers accepted. If we could not find statis-

tically significant elasticity estimates, we also used a

logical hierarchical approach.

Competitive Rate Shopping

Dynamically shopping forward-looking rates of our

competitors is a critical component of the price opti-

mization module. We find publicly available com-petitor rates on the Internet and through third-party

sources (e.g., TravelClick and Rubicons MarketVision

reports), select a maximum of four hotels as com-

petitors, and shop each of the four competitors each

night. In specific regions (e.g., Greater China and

Asia Australasia), we also consider booking channels

in collecting shopping data. Each day, the optimiza-

tion engine uses the shopping data to optimize the


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rates for the next 350 days. If we were to undertake

shopping all our competitors for their unrestricted

rate products for the entire enterprise, our cost would

be millions of dollars per year, an infeasible expense.In addition, our shop requests would flood the Web

and global distribution systems, bringing reservations

for IHG and other hotels to a halt. Our shopping bud-

get allowed us to afford only 2030 shops each day for

each competitor. Given the budget and distribution

constraints, we developed a random, stratified sam-

pling strategy that allows us to recommend shops by

blending future booking activity and historic booking

patterns. If a product has a high booking activity (sim-

ilar to some special event days), then the probability

of that product being shopped is higher. Historically,

if a product has been shopped frequently, then it ismore likely to be shopped.

We do only 2030 shops each day; however, to

recommend optimal rates, the optimization module

requires shop data for each of the next 350 days.

Therefore, we developed a reasonable approach to

fill in the dates for which we have not shopped.

This method considers day-of-week patterns, LOS

patterns, and last-shopped time stamps; fills in the

missing dates; and generates the full list of shopping

data to complete the rate data set before entering it

into the optimization engine. If not for this random,

stratified sampling approach and a novel way to fill in

missing rates, we would have had to spend millions

of dollars to acquire the necessary shop data.

Benefits Estimation

Price optimization as a business capability is not

complete without measuring the revenue benefits.

Both hotel revenue managers and senior executives

impressed upon us the need for a rigorous mea-

surement methodology that measured the impact of

price optimization. Our method involved compar-

ing the change in a key metric (REVPAR) for a test

period and a baseline period for the properties using

price optimization and for the control properties not

using it. We conducted statistical studies to account

for statistical significance of such a REVPAR uplift.

In selecting the control properties, we considered sea-

sonality, brand, business segmentation mix, hotel type

(e.g., business, leisure, convention), and location type

(e.g., downtown, suburban, airport). The statistical

significance tests ensured that the price optimization

related coefficients were significant. This study found

a 2.7 percent increase in REVPAR for the beta test

properties.We performed several iterations of this approach

in which we primarily addressed the logic of con-

trol property selection and the key driver variables,

which could vary by property. We then socialized this

approach with key stakeholders and the executives

who were involved in the initial test. The stakeholder

involvement and qualitative feedback from the users

was instrumental in the inclusion of PERFORM price

optimization in the 2009 IHG annual review. For the

properties that had used this module for the previous

12 months, a 2.7 percent increase in REVPAR trans-

lates to a revenue increase of $145 million. At fullrollout, we anticipate that this capability will generate

approximately $400 million per year.

Rate Expansion and Fill-In Logic

As we described in the Competitive Rate Shoppingsub-

section, we could shop only a small fraction of future

competitor prices for arrival dates and LOS dates.

To determine optimal prices, we needed an estimated

price for each competitor for every arrival date and

LOS combination for the next 350 arrival dates. There-

fore, we developed an algorithm to expand the actual

competitive shops to the full cardinality of reserva-tions products. Although we cannot share the specific

details of this algorithm, we can describe its general

principles.

If we did not shop a product on a given day, but

had shopped it in the previous few days, we infer an

observed price. We fill in the remaining holes in the

competitive rate matrix with shops of a different LOS

for the same arrival date, and fill in any remaining

holes with rates from adjacent arrival dates. Overar-

ching the algorithm is an inherent bias toward shorter

LOS dates. The rationale behind this bias is that

nearly 40 percent of bookings are for multiple nights(the average LOS is about 2.0 days). Also, the gen-

eral tendency (although definitely not the rule) in the

hotel industry is that the rate for a multiple-night stay

is the sum of the one-night-stay rates.

Optimization Model

The core price optimization model is innovative in

the industry. Modeling demand as a function of


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price requires that the objective function is nonlinear.

Special reservation rules, which are unique to IHG,

require logical and integer constraints. The model

accounts for LOS patterns, significantly increasing itscomplexity relative to a staynight model. We for-

mulated the optimization model as a mixed-integer,

bilinear mathematical program. We implemented a

special optimization method that leverages CPLEX

to iteratively solve approximately 1,000 integer pro-

grams per day for each property. On average, rates

are generated for each property six times per day. The

price optimization module solves four million linear

programs each day. Appendix A provides details on

the optimization model; Appendix B provides details

on benefits measurement.

Price Optimization Spawns NewRevenue Management InitiativesThe price optimization project has reinvigorated RM

at IHG. As we previously mentioned, the imple-

mentation of targeted enhancements to the existing

PERFORM system was an early outcome. Price opti-

mization also inspired a multimillion-dollar initiative

to revamp HOLIDEX. A new central reservation sys-

tem, REVOLUTION, will streamline the definition of

rate products and ensure that a rational rate struc-

ture is in place at all hotels. The price optimization

project also raised the visibility of the RM groups

forecasting expertise within the global IHG organiza-

tion. The RM group is now considered the corporate

forecasting center of excellence, and the development

of an enterprise-wide forecasting platform, predictive

demand intelligence (PDI), is a testament to that. PDI

generates a forecast that integrates with the key cor-

porate business functions of finance and marketing

and with property-based RM, including PERFORM.

By using a common forecast, IHG is better able to align

marketing and budgeting with the tactical pricing and

inventory control RM processes. We deployed a PDIprototype in the fourth quarter of 2010; the production

system is currently under development. Also, because

we deployed price optimization to a majority of prop-

erties, we can no longer measure the uplift of price

optimization using control properties; hence, a project

is underway to construct a performance measurement

model, which will be similar to the model we devel-

oped during the initial research and scoping phase.

It will use simulation to estimate the revenue uplift

from pricing actions, and generate insights to support

continuous improvements in forecasting and pricing.

Concluding RemarksThe journey to develop PERFORM with price opti-

mization at IHG provided many lessons on how to

build a business case for a massive enterprise sys-

tem with OR models at its core. The research and

scoping project built sufficient momentum to help

us gain funding for the development of a prototype

and live market test. The live market test and rigor-

ous test and control benefits measurement provided

the foundation of an unassailable business case and

funding for a multimillion-dollar software develop-

ment and business transformation project, which a

committee of IHG senior executives approved. IHG

RM, IT, and operations teams partnered to develop

and deploy the price optimization solution to a global

hotel enterprise. To date, price optimization has gen-

erated $145 million of incremental revenue for IHG

and its franchise partners.

IHGs price optimization system is already having

a major impact on the hotel industry. Other leading

global hotel enterprises are currently developing their

own LOS price optimization solutions. Carlson Hotels

is implementing a staynight price optimization solu-

tion (Rozell 2009). The methods for estimating price

response developed at IHG are applicable to many

industries. The specific problem of optimizing price

for demand based on LOS is directly transportable to

rental cars (length of rental) and airlines (origin and

destination). The IHG experience also helped inspire

the development of similar methods to optimize the

price of juice drinks in a resource-constrained supply

chain (Bippert 2009).

Appendix A. Optimization Model

Formulation and Solution MethodologyOur model is an adaptation to the hotel LOS prob-

lem that Gallego and van Ryzin (1997) proposed for

the airline network problem. The plain formulation

without business rules is listed below.

Variables

Rad: Rate for arrival date a and LOS d. (Optimiza-

tion decision variable.)


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Dad =f RadCRad: Demand generated for arrival

date a; LOS d is a function of the hotels rates (Rad)

and competitor rates (CRad).

cos tad: Room turn cost for arrival dateaand LOSd.L: Set of all resources; resources are the stay dates

with available capacity.

Cl: Set of all arrival date and LOS combinations

consuming resource l .

Cl: Available capacity of stay date l .

The contribution function that IHGs price opti-

mization optimizes follows.

Max contribution=

ad

Dad Rad cos tad

whereDad= f RadCRad

subject to

adCl

Dad Cl l L

Rad 0 ad

Because both demand and prices are decision

variables, we implemented a special optimization

method using a decomposition heuristic that lever-

ages CPLEX. We believe the decomposition heuris-

tic is better than a Dantzig-Wolfe decomposition

approach or a dynamic programming approachbecause the optimal prices in the near future must be

more accurate when compared to prices farther out in

the decision horizon, especially when we look at the

booking profile of IHG guests.

Special reservation rules within the IHG business

environment require integer variables and logical con-

straints, greatly complicating the optimization model.

Appendix B. Benefits EstimationWe described the benefits estimation methodology in

theBuilding the Business Casesection, depicted it in Fig-

ure 5, and described the variables for which we con-

trolled in the Production System Development, Deploy-

ment Plan, and Revenue Uplift Estimates from Beta Release

Properties section. However, we cannot underestimate

the degree of sophisticated analysis that was required

to design these controlled experiments and statistically

analyze the results. We computed REVPAR changes

from the baseline to the test period for both prototype

and beta properties. Despite our best efforts to con-

trol the variability, REVPAR changes were extremely

volatile. Therefore, we used a Pearsons chi-squared

test to test our hypothesis that price optimizationproperties outperformed their control group. If the

price optimization property REVPAR change was bet-ter (i.e., a larger increase or smaller decrease) than that

of a control property, we counted that treatment con-trol pair as a win for price optimization; otherwise, we

counted it as a loss. Comparing the frequency of wins

and losses, we computed a chi-squared test statisticfor the hypothesis with much more power than a sim-

ple means test. For the beta properties, we observed

41 wins and 27 losses, resulting in a confidence fac-tor of 91 percent that the price optimization prop-

erties performed better than their control properties.The test of means showed a 2.7 percent improvementin REVPAR with a confidence of 80 percent that the

improvement was greater than zero. Typically, a simu-

lation methodology is used to measure the benefits forsuch a capability (Smith et al. 1992). However, mea-

suring the benefits using a test versus control group

of hotels is a reliable methodology. Because of themethodologys rigor in selecting control groups and

the involvement of stakeholders in the benefits mea-

surement process, we were able to explain the benefitscase to the senior executives.

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