Retail Price Optimization at InterContinental Hotels Group
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Vol. 42, No. 1, January–February 2012, pp. 45–57
ISSN 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 InterContinental
Hotels Group
Dev Koushik
Intercontinental Hotels Group, Atlanta, Georgia 30346, dev.koushik@ihg.com
Jon A. Higbie
Revenue Analytics, Atlanta, Georgia 30339, jhigbie@revenueanalytics.com
Craig Eister
Intercontinental Hotels Group, Atlanta, Georgia 30346, craig.eister@ihg.com
PERFORMSM with price optimization is the first large-scale enterprise implementation of price optimization in
the 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 incremental
revenue 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.
I nterContinental Hotels Group (IHG) is the world’s
largest hotel group based on number of rooms.
Each hotel, including corporate-owned and man-
aged properties, is responsible for its own profit and
Through its various subsidiaries, IHG owns, man- loss, essentially operating as an independent busi-
ages, leases, or franchises over 4,500 hotels and more ness. Each hotel’s revenue manager is responsible
than 650,000 guest rooms in nearly 100 countries for optimizing that hotel’s revenue performance by
and territories worldwide. It owns a portfolio of undertaking key revenue strategies with respect to
well-recognized and respected hotel brands, including pricing and inventory management. They include
InterContinental Hotels, Hotel Indigo, Crowne Plaza demand forecasting, inventory control management
Hotels and Resorts, Holiday Inn Hotels and Resorts, (overbooking and length-of-stay (LOS) controls), price
Holiday Inn Express, Staybridge Suites, and Can- execution (rate implementation and adjustments), and
dlewood Suites. It also manages the world’s largest collaboration with the hotel’s general manager on
hotel loyalty program, Priority Club Rewards, which strategy and business planning. Some hotels have an
has 52 million members worldwide. Approximately on-site dedicated revenue manager, titled a director of
85 percent of IHG’s hotels are franchised, 14 percent revenue management (DORM); other hotels are part
are managed, and 1 percent are owned. of a corporate revenue management services group,
45Koushik, Higbie, and Eister: Retail Price Optimization at IHG 46 Interfaces 42(1), pp. 45–57, © 2012 INFORMS which manages pricing and inventory on behalf of the hotel profits fell by $642 million (Bowers and Freitag hotels and is generally located remotely. 2003). Soft demand for hotel rooms also lessened the A corporate revenue management (RM) team sup- benefits of the PERFORM system because the ben- ports the hotel revenue managers by providing sys- efits of yield management models primarily derive tems, strategy, and a support organization of regional from tightening inventory controls when demand is DORMs and geographic divisional vice presidents strong. Internet booking channels created increasing (e.g., for North America and the Asia Pacific regions). price transparency, allowing consumers to compari- IHG’s globally distributed RM organizational struc- son shop multiple hotels to find the best deal. Price ture is the norm for the hotel industry; however, it dif- transparency and the need to drive demand con- fers from the highly centralized RM structure in the tributed to the erosion of rate fences (restrictions), airline industry. The complex organizational struc- which are essentially qualifications on bookings that ture of hotel RM presents significant challenges with support segmented demand and pricing. The erosion respect to training, adoption, and consistent execution of rate fences undermined the RM assumption of of RM strategies and system use. independent demand. The application of RM in the hotel industry was The hotel industry has traditionally divided de- adapted from airline industry RM systems, which mand into two broad segments: group and transient. the industry began to implement in the 1980s (Cross The group segment includes conferences and corpo- et al. 2009). Since their inception, hotel RM systems rate events for which a hotel contracts with a group have opened and closed rate products, the prices of to commit large blocks of rooms for a specific period. which are predetermined via manual processes, with- The transient segment represents all individual book- out analytics. These systems assume that demand ings. The objective of hotel RM systems is to opti- by rate segment is independent, an assumption that mize revenues for the transient segment. Although is not true and can lead to a downward spiral in many RM systems also include a group yield mod- rates when demand is soft (Cooper et al. 2006). This ule, transient and group segments are managed sepa- approach is similar to that of the early airline models rately. Lee et al. (2011) divided the transient demand (Smith et al. 1992), which open and close fare classes into retail and negotiated segments. Negotiated seg- under the assumption that demands by fare class are ments include corporate special rates for large cus- independent. tomers (e.g., IBM and HP). These rates are typically PERFORMSM is a Web portal through which more fixed and are not subject to dynamic price changes. than 4,000 users worldwide access IHG’s RM sys- Most also have last-room availability clauses; thus, tem and related tools. Like the RM systems at other they are not subject to the inventory controls that RM major hotel enterprises and prior to implementing systems generate. Only the retail segment is subject price optimization, PERFORM optimized availability to the full range of pricing and inventory controls. and LOS inventory controls based on the assump- Lee et al. (2011) further segmented retail demand into tion of independent demand. The deterministic model restricted and unrestricted segments. In a study of described by Baker and Collier (1999) is the most com- 2006–2007 hotel demand, these authors demonstrated mon formulation used in practice. Like that used by that rates paid by unrestricted retail customers do not some other hotel RM systems, the PERFORM yield tend to increase as the day of arrival approaches and management optimization model was a variant of the that restricted rates actually tend to decrease. This deterministic model with stochastic demand. observation is contrary to the long-held belief that cus- The growth of Internet booking channels starting tomer willingness to pay increases as the day of arrival about 2000, the deepening travel recession starting approaches. A cornerstone of yield management and about 2001, and the tragic events of September 11, 2001 RM systems is the assumption that higher-booking combined to drive hotel RM systems to incorporate customers book late in the booking cycle. Lee et al. pricing as well as inventory yield techniques (Cross (2011) assert—and the authors of this paper agree— et al. 2009). Hotel occupancy rates fell by 15–20 per- that segmenting hotel demand into group and retail cent at leading hotel groups (Cross et al. 2009), and US segments better aligns with how consumers view
Koushik, Higbie, and Eister: Retail Price Optimization at IHG
Interfaces 42(1), pp. 45–57, © 2012 INFORMS 47
hotel products and that the study’s findings seriously based on price sensitivity. A new price optimization
challenge the assumption of independent demand by module in PERFORM would support pricing analysis,
rate segment and the assumption that willingness to recommend prices, adjust forecasts based on IHG’s
pay increases as the day of arrival approaches. own and competitive prices, and automate price exe-
The decline in hotel demand, the rise of Internet cution. New reporting and a new interface to execute
booking channels and price transparency, and chal- prices in HOLIDEX Plus would be essential.
lenges to underlying RM assumptions drove changes
in hotel RM workflow. IHG adapted to this changing Building the Business Case
environment by revamping its pricing strategy, shift-
Designing and implementing the price optimization
ing the focus from an inventory allocation approach
capability was a major cross-functional effort. Corpo-
to a pricing focus. It implemented a rational pricing
rate RM led the analysis and design, IHG’s informa-
structure within which restricted retail discount rates
tion technology (IT) group developed the new screens
were tied to the unrestricted best flexible rate (BFR).
and reports, and a training group developed and
A uniform rational rate structure facilitated dynamic
delivered a global training program. IHG corporate
pricing.
and franchise hotel management needed to buy into
IHG, without an automated capability to opti-
mize prices, undertook a process of educating its the changes. The executive leadership team needed to
hotel staffs on the need to flex their BFRs based on approve the large corporate capital outlay and support
demand. Because of this strategic shift, IHG property- the massive change management process. However,
based DORMs were spending, on average, more management had been burned many times by large
than 30 percent of their time gathering competitive capital expenditures that failed to deliver promised
price intelligence. This intelligence included competi- benefits; therefore, the executive team wanted quan-
tor rates, which they found on the Internet or through tifiable proof that implementing the price optimization
third-party sources, including TravelClick and Rubi- capability would increase profits.
con’s MarketVision reports. DORMs changed rates In the third quarter of 2006, IHG engaged Rev-
through IHG’s central reservations system, HOLIDEX enue Analytics. The two organizations formed a part-
Plus. HOLIDEX Plus is a mainframe system, which nership in which they jointly conducted a research
was not designed to facilitate frequent rate changes; and scoping project that lasted through the design,
its pricing mechanism is cumbersome and time con- development, and deployment of the price optimiza-
suming. As a result, pricing analysis was ad hoc, tion module. The project’s goals were to demonstrate
response to competitive actions slow, and execution the feasibility of price optimization, develop an ini-
inconsistent. Forecasting represented 30 percent of a tial estimate of its potential benefits, and identify other
DORM’s time, much of it to adjust forecasts for rate capabilities that needed to be upgraded to enable
changes in the DORM’s own property and in compet- price optimization. The project team developed a sim-
itive rates. DORMs spent only 20 percent of their time ulation model, which estimated the theoretical ben-
performing more strategic analysis and business plan- efits of price optimization to be from 2.75 percent
ning and less than 10 percent in managing inventory to 6 percent revenue uplift on the retail segment.
controls. The desired division of time among tasks It also determined that it needed to upgrade the
is 40 percent in forecasting, 40 percent in strategy, existing RM forecast algorithms. Transient forecast
10 percent in pricing, and 10 percent in inventory con- errors reduced the benefits of optimizing price by
trol. Corporate RM realized that the DORMS needed 1.3 percent. Group forecast errors reduced benefits by
system support for pricing (including price-adjusted 0.7 percent. Figure 1 shows the sensitivity of the price
forecasting) to better use their time, to improve the optimization simulation to forecast error by elasticity.
quality of pricing decisions, and to facilitate improved Based on this analysis, the team immediately
execution of pricing best practices. A key part of the launched projects to improve the PERFORM RM fore-
solution was a price optimization capability that opti- cast models.
mizes prices for the retail segment, considers com- Although the research and scoping phase required
petitor rates, and generates a price-adjusted forecast extensive applications of analytics, a few simpleKoushik, Higbie, and Eister: Retail Price Optimization at IHG
48 Interfaces 42(1), pp. 45–57, © 2012 INFORMS
Price optimization benefit average uplift for a sample of 776 properties
12.0
Price optimization benefit
Impact of group forecast error
10.0
Impact of group and transient forecast error
8.0
Revenue uplift (%)
6.0
4.0
2.0
0.0
– 2.0
– 0.8 –0.9 – 1.0 – 1.1 –1.2 –1.3 –1.4 –1.5 –1.6 –1.7 –1.8 –1.9 –2.0
Elasticity
Figure 1: Transient and group forecast errors significantly reduced the benefits of price optimization.
models were decisive in communicating optimiza- managers faced? The pricing game was key in secur-
tion concepts to the chief marketing officer (CMO), ing the funding we needed.
brand presidents, and other senior executives. The In the second quarter of 2007, we received fund-
only way to accurately communicate the form of ing approval for a high-level design and live market
the price optimization model is through mathematics, test project. The market test required construction
which is not the preferred method of explanation for of a working price optimization prototype, which
most senior executives. We found that the simple two- we would deploy to a limited number of hotels.
dimensional example depicted in Figure 2 was pow- These hotels would use the prototype system to man-
erful in explaining concepts to the executives whose age rates for their hotels for the duration of the
approval we needed to fund our price optimization test. In addition to providing valuable feedback from
project. DORMs on the design, the market test would serve as
To help us in gaining executive approval, we a robust measure of the achievable benefits. The exec-
decided to build a simple interactive simulation model utive team and capital committee demanded proof
in the form of a game (see Figure 3) in which the audi- from live market tests, not merely theoretical esti-
ence would try to guess what the optimal price should mates from a simulation. The IHG capital committee,
be. The base-case scenario formed a business-as-usual which includes the most senior IHG executives—the
point of reference. For each turn of the game, competi- CEO, the CFO, the CMO, and at least one regional
tor rates, demand, and capacity varied; the object was president, is responsible for releasing funding for
to guess the rate that would optimize revenue. The large investments. The high-level design would pro-
game was fun, but also communicated the challenges vide enough detail to enable the IT group to esti-
revenue managers faced in determining the best rate mate price optimization development costs. As part
for a single date. It reminded the audience that rev- of the high-level design, the combined IHG and RA
enue managers had to handle multiple-rate products operations research (OR) teams would research and
for 350 future arrival dates, while accounting for LOS prototype the four other price optimization models:
interactions. If senior executives could not guess the elasticity and price-sensitive demand forecast, LOS
right price in this simple game, how much more price optimization model formulation and solution
challenging was the problem that the hotel revenue algorithm, competitive rate shopping algorithm, andKoushik, Higbie, and Eister: Retail Price Optimization at IHG
Interfaces 42(1), pp. 45–57, © 2012 INFORMS 49
Contribution function
500 30,000
450
25,000
400
350
Demand (room nights)
20,000
Contribution ($)
300
250 15,000
200
10,000
150
100 Demand
Unconstrained contribution 5,000
50 Constrained contribution
– –
50 75 100 125 150 175 200
Room rate ($)
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.
Holiday Inn-Highway Location-Tuesday
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
Best
Best guess Optimal HI
Comfort Best Expected HI guess HI rooms Optimal HI rooms Guess
Quality Inn Courtyard Inn Western demand capacity rate sold rate sold revenue
$79.00 $109.00 $89.00 $99.00 38 40 $99 36 $91.10 40 $3,552.86
k to clear Winner? Optimizer
Margin of
a for answer victory 2.6%
Figure 3: The pricing game was one of the most compelling tools we used to communicate the need for price
optimization to senior executives. We challenged the executives to guess the revenue optimal price under varying
supply, demand, and competitive pricing conditions. In the game, we show the key input parameters and the
recommended rates using a glass-box solution framework; next to the recommended price, we present key data
elements (e.g., occupancy, current IHG and competitor prices, IHG and competitor reference prices, and price
sensitivity) to validate the price recommendations.Koushik, Higbie, and Eister: Retail Price Optimization at IHG
50 Interfaces 42(1), pp. 45–57, © 2012 INFORMS
the competitive rate fill-in logic. To implement the the books and a ramp-up period, we excluded the first
prototype and conduct the live market test, we had four weeks of the pilot, leaving a 12-week test period.
to implement all these models, which we describe in For each of the 13 treatment properties, we selected
the Core OR Models section and the appendices. 1 to 4 control properties (34 control properties in all).
To expedite the prototype development, we Variables controlled for included brand, region, prop-
decided to simplify the optimization to a staynight erty size, and group mix. The 12 weeks prior to the
model, which essentially assumes that all demand is prototype roll-out were the baseline period. We con-
for only one night. The production system would be trolled for day of week by ensuring that the baseline
an LOS model, which recognizes that guests can stay and test periods had equal numbers of each day of the
for multiple nights. Modeling multiple-night stays week. We assumed that seasonality was the same for
requires a network structure for the constraint matrix the prototype and control properties. Figure 5 illus-
to account for contention of different LOS periods for trates the concepts of baseline and test periods and of
the same room on a given night. prototype and control properties.
Figure 4 shows the prototype’s structure. We imple- The benefit metric we used was total revenue per
mented the prototype in Excel VBA, connected it to an available room (REVPAR), which is the total revenue
Oracle database (the prototype DB), and refreshed this divided by the number of room nights available for
database weekly from IHG’s enterprise data ware- sale. Although the price optimization function only
house (EDW) and the PERFORM RM tables. recommended retail price changes, total REVPAR
In July 2007, we deployed the first prototype to includes group and negotiated segments for these
a hotel; eventually, we deployed it to 18 properties. reasons. (1) REVPAR is the most important per-
We conducted the live market test on 13 properties formance metric because the executives and capital
over a 16-week period. To account for reservations on committee understand it clearly. (2) We considered,
but rejected, transient REVPAR (transient revenue
Competitive rates 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 sold
EDW Perform/RM and rates; therefore, some price optimization bene-
fits are expected in these segments. Using a Pearson’s
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-
Prototype DB 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
PO prototype
worksheets 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-
Figure 4: The prototype consisted of seven user screens and three screens tinental Madrid, wrote “Great Tool! [It] challenges
to allow an administrator to gather and report usage statistics. you to question your pricing practices.” We collectedKoushik, Higbie, and Eister: Retail Price Optimization at IHG
Interfaces 42(1), pp. 45–57, © 2012 INFORMS 51
Prototype property Baseline period Excluded Test period
Pilot launch
Control property Baseline period Test period
Time (weeks)
Figure 5: The test period was 12 weeks starting at week five following the prototype launch. The baseline period
covered the 12 weeks prior to prototype launch.
detailed user feedback, incorporated it into the tool, estimate of the prototype. The benefits of the proto-
and performed additional analytics on usage statis- type were so substantial that some DORMs pleaded
tics. Figure 6 shows the utilization of the various to keep the prototype running. As a result, we were
prototype screens. The staynight screen was the most able to build a solid business case for a production
heavily used. This guided the design of the optimize version of the price optimization capability. In the
price screen in the production system (see Figure 9). fourth quarter of 2007, based on the detailed benefits
The calendar view was woven into the overall nav- estimate, the support of hotel general managers, and
igation. The workbench screen proved important as recommendations of the property and regional rev-
users gained familiarity with the system; however, enue managers, the IHG capital committee approved
because the optimize price screen was so critical, we a multimillion dollar budget to develop the price opti-
had to add quick links into the production system to mization module within PERFORM.
allow the users to navigate directly to this screen.
The live market test provided a rigorous benefits Production System Development,
estimate for the new price optimization capability.
Deployment Plan, and Revenue Uplift
The prototype and the high-level design enabled us
to reliably estimate the time and cost required to con-
Estimates from Beta Release Properties
struct the production system. Feedback from proto- Development began in January 2008. The OR team
type users added weight to the quantitative benefits implemented the market response model (MRM),
competitive rate shopping module, the rate expan-
sion module, and the core price optimization engine.
Reservations Bus. rules
8% 5% Calendar IT implemented the data model, the server that inte-
7% grates all modules, the user interface, job scheduling,
and the configuration of new servers for the price
Analysis Workbench optimization capability. The RM strategy team and
11% 26% the OR team developed and implemented the change
management plan and the rollout plan and worked
Comp. rates with the training group to develop training modules.
11% The MRM describes the relationship between
demand and other driver variables. The competitive
rate shopping module specifies which future arrival
Staynight
32%
dates and LOS products should be shopped. Shop-
ping all future combinations of arrival date and LOS
Figure 6: The chart shows the relative utilization of the prototype screens.
would overburden the global hotel distribution sys-
This feedback from the prototype helped guide the design of the produc- tem; thus, it is not feasible. Because only a sample of
tion system. future arrival dates and LOS periods are shopped, theKoushik, Higbie, and Eister: Retail Price Optimization at IHG
52 Interfaces 42(1), pp. 45–57, © 2012 INFORMS
Rate shopping Shop requests Market vision™
competitor rates
Comp rates
Rate expansion
Competitive
EDW
rates
Server
MRM
Optimization
engine
Figure 7: The flowchart shows the logical relationship between the rate-shopping module, the MRM module, the
price optimization engine, and the server in the price optimization architecture.
rate expansion module infers rates for products that recommendations that the yield optimization module
are not shopped. The price optimization engine builds generated were good. (4) Pricing decisions and yield
the optimization model formulation from the input decisions are made at different frequencies. Inventory
data and solves for optimal prices. Figure 7 depicts controls change constantly and in real time as book-
the relationship between the core modules. ings are made; however, hotels prefer to change prices
We used a decomposition approach to model less frequently. (5) Implementing price optimization
demand as a function of price; we modeled it as would require extensive retraining of revenue man-
independent of price and then modeled the remain- agers and careful configuration of each property,
ing variability in demand as a function of price. This necessitating a staged rollout. Therefore, the existing
approach fit the data well and aligned with the deci- PERFORM system would have to continue to function
sion to leverage existing PERFORM RM modules to for other properties during the rollout. (6) Leveraging
the fullest extent possible. In particular, we wanted the existing modules would accelerate delivery of the
to continue to use the existing forecasting and yield new system.
optimization functions. Price optimization works in conjunction with the
There were six fundamental reasons for continu- existing PERFORM forecasting and yield optimiza-
ing to use the existing PERFORM modules. (1) Rev- tion components. The MRM modifies the PERFORM
enue managers were already effectively using much forecast at the optimal prices to make it price sen-
of the PERFORM functionality, including user screens sitive. The price-neutral unconstrained demand fore-
and reports. (2) The existing forecast, although not cast, available capacity, and competitive rates are the
price sensitive, was reasonably accurate. (3) The LOS key inputs to the price optimization engine. PluggingKoushik, Higbie, and Eister: Retail Price Optimization at IHG
Interfaces 42(1), pp. 45–57, © 2012 INFORMS 53
Price-sensitive
forecast
Forecasting engine Yield optimization engine
Price-insensitive forecast
Optimal length of stay CRS
Price-sensitive forecast
Optimal prices
Price optimization engine
CRS— Centralized reservation system
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- revenue managers at beta test properties began simul-
sensitive demand forecast at the new rates. After taneously. Beta testing in the production environment
the rates have been updated, the yield optimization also began, and continued until the third quarter of
engine uses the price-sensitive demand forecast to 2009. Starting in the fourth quarter of 2009, price
update the LOS inventory controls at the new rates. optimization rollout began for the rest of the IHG
Figure 8 depicts how PERFORM’s forecasting, yield properties at a rate of approximately 100 per month.
optimization, and price optimization engines work In the third quarter of 2009, after beta properties had
together. been using the capability for several months, we con-
RM executives insisted that the solution not be a ducted a benefits measurement study similar to that
conducted following the prototype market test. This
black box. Presenting the critical components driv-
study showed a 2.7 percent increase in REVPAR for
ing the pricing recommendation with the recom-
the beta test properties. As of this writing, more than
mendations was critical. Data presented include the
2,000 properties worldwide are running PERFORM’s
three pillars of pricing—competitive rates, forecasted
price optimization, and we add about 100 proper-
occupancy, and price-sensitivity ratings derived from
ties each month. The global nature of this capability
elasticity estimates. Figure 9 shows a screenshot of means that some core OR models for shopping com-
PERFORM’s optimize price tab, which is used to petitive rates must be modified to also account for
review price recommendations and publish them to booking channels.
the central reservation system. Training on the three
pillars, including a variation of the game we devel- Core OR Models
oped in the research and scoping phase, is a critical
Price optimization development involved intensive
prerequisite that revenue managers must meet before OR modeling. Given the scope of this paper, we
using the price optimization functionality. This train- cannot describe all the work in detail; however, in
ing and the need to carefully configure each prop- this section we will outline five key areas in which
erty’s competitive set—the set of competitors whose we applied OR—MRM, competitive rate shopping,
rate data need to be shopped (a critical input to price benefits estimation, rate expansion and fill-in logic,
optimization)—are the two main reasons the price and optimization model. The IHG OR and Revenue
optimization rollout had to be gradual. Analytics OR teams designed and implemented each
Alpha testing for price optimization began in the model and then integrated it with the server and user
first quarter of 2009. Training of regional DORMs and interface, which IHG IT developed.Koushik, Higbie, and Eister: Retail Price Optimization at IHG
54 Interfaces 42(1), pp. 45–57, © 2012 INFORMS
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 remaining
capacity, remaining demand, competitor rates, and our current BFR, revenue managers can intuitively judge the
reasonableness of the price recommendations. As suggested by the other tabs, price optimization also provides
a capability to drill down into demand forecasts, competitive rates, current bookings, and additional pricing
analysis.
Market Response Model (MRM) that aligned with key business segments at which
The MRM describes demand as a function of price and elasticity estimates were significant and that the rev-
other driver variables. Because conducting real-time enue managers accepted. If we could not find statis-
price experiments to estimate price sensitivity is dif- tically significant elasticity estimates, we also used a
ficult in IHG’s distributed environment, we decided logical hierarchical approach.
to use pseudo-random price experiments to mine his-
Competitive Rate Shopping
torical prices, historical demand, and historical com-
Dynamically shopping forward-looking rates of our
petitor rates to measure the response of the demand
competitors is a critical component of the price opti-
changes against IHG and competitor price changes. mization module. We find publicly available com-
A key input from the hotel revenue managers was that petitor rates on the Internet and through third-party
if they were to have greater acceptability of the price sources (e.g., TravelClick and Rubicon’s MarketVision
optimization capability, modeling demand as a func- reports), select a maximum of four hotels as com-
tion of competitor rates would also be imperative. petitors, and shop each of the four competitors each
Within the MRM module, we modeled demand night. In specific regions (e.g., Greater China and
as a function of price and competitor rates to com- Asia Australasia), we also consider booking channels
pute price elasticity. We tried various segmentation in collecting shopping data. Each day, the optimiza-
schemes and decided on a segmentation approach tion engine uses the shopping data to optimize theKoushik, Higbie, and Eister: Retail Price Optimization at IHG
Interfaces 42(1), pp. 45–57, © 2012 INFORMS 55
rates for the next 350 days. If we were to undertake significance tests ensured that the price optimization
shopping all our competitors for their unrestricted related coefficients were significant. This study found
rate products for the entire enterprise, our cost would a 2.7 percent increase in REVPAR for the beta test
be millions of dollars per year, an infeasible expense. properties.
In addition, our shop requests would flood the Web We performed several iterations of this approach
and global distribution systems, bringing reservations in which we primarily addressed the logic of con-
for IHG and other hotels to a halt. Our shopping bud- trol property selection and the key driver variables,
get allowed us to afford only 20–30 shops each day for which could vary by property. We then socialized this
each competitor. Given the budget and distribution approach with key stakeholders and the executives
constraints, we developed a random, stratified sam- who were involved in the initial test. The stakeholder
pling strategy that allows us to recommend shops by involvement and qualitative feedback from the users
blending future booking activity and historic booking was instrumental in the inclusion of PERFORM price
patterns. If a product has a high booking activity (sim- optimization in the 2009 IHG annual review. For the
ilar to some special event days), then the probability properties that had used this module for the previous
of that product being shopped is higher. Historically, 12 months, a 2.7 percent increase in REVPAR trans-
if a product has been shopped frequently, then it is lates to a revenue increase of $145 million. At full
more likely to be shopped. rollout, we anticipate that this capability will generate
We do only 20–30 shops each day; however, to approximately $400 million per year.
recommend optimal rates, the optimization module
Rate Expansion and Fill-In Logic
requires shop data for each of the next 350 days.
As we described in the Competitive Rate Shopping sub-
Therefore, we developed a reasonable approach to
section, we could shop only a small fraction of future
fill in the dates for which we have not shopped.
competitor prices for arrival dates and LOS dates.
This method considers day-of-week patterns, LOS
To determine optimal prices, we needed an estimated
patterns, and last-shopped time stamps; fills in the
price for each competitor for every arrival date and
missing dates; and generates the full list of shopping
LOS combination for the next 350 arrival dates. There-
data to complete the rate data set before entering it fore, we developed an algorithm to expand the actual
into the optimization engine. If not for this random, competitive shops to the full cardinality of reserva-
stratified sampling approach and a novel way to fill in tions products. Although we cannot share the specific
missing rates, we would have had to spend millions details of this algorithm, we can describe its general
of dollars to acquire the necessary shop data. principles.
If we did not shop a product on a given day, but
Benefits Estimation
had shopped it in the previous few days, we infer an
Price optimization as a business capability is not
observed price. We fill in the remaining holes in the
complete without measuring the revenue benefits.
competitive rate matrix with shops of a different LOS
Both hotel revenue managers and senior executives
for the same arrival date, and fill in any remaining
impressed upon us the need for a rigorous mea-
holes with rates from adjacent arrival dates. Overar-
surement methodology that measured the impact of
ching the algorithm is an inherent bias toward shorter
price optimization. Our method involved compar-
LOS dates. The rationale behind this bias is that
ing the change in a key metric (REVPAR) for a test
nearly 40 percent of bookings are for multiple nights
period and a baseline period for the properties using (the average LOS is about 2.0 days). Also, the gen-
price optimization and for the control properties not eral tendency (although definitely not the rule) in the
using it. We conducted statistical studies to account hotel industry is that the rate for a multiple-night stay
for statistical significance of such a REVPAR uplift. is the sum of the one-night-stay rates.
In selecting the control properties, we considered sea-
sonality, brand, business segmentation mix, hotel type Optimization Model
(e.g., business, leisure, convention), and location type The core price optimization model is innovative in
(e.g., downtown, suburban, airport). The statistical the industry. Modeling demand as a function ofKoushik, Higbie, and Eister: Retail Price Optimization at IHG
56 Interfaces 42(1), pp. 45–57, © 2012 INFORMS
price requires that the objective function is nonlinear. It will use simulation to estimate the revenue uplift
Special reservation rules, which are unique to IHG, from pricing actions, and generate insights to support
require logical and integer constraints. The model continuous improvements in forecasting and pricing.
accounts for LOS patterns, significantly increasing its
complexity relative to a staynight model. We for- Concluding Remarks
mulated the optimization model as a mixed-integer,
The journey to develop PERFORM with price opti-
bilinear mathematical program. We implemented a
mization at IHG provided many lessons on how to
special optimization method that leverages CPLEX
build a business case for a massive enterprise sys-
to iteratively solve approximately 1,000 integer pro-
tem with OR models at its core. The research and
grams per day for each property. On average, rates
scoping project built sufficient momentum to help
are generated for each property six times per day. The
us gain funding for the development of a prototype
price optimization module solves four million linear
and live market test. The live market test and rigor-
programs each day. Appendix A provides details on
ous test and control benefits measurement provided
the optimization model; Appendix B provides details
the foundation of an unassailable business case and
on benefits measurement.
funding for a multimillion-dollar software develop-
ment and business transformation project, which a
Price Optimization Spawns New committee of IHG senior executives approved. IHG
Revenue Management Initiatives RM, IT, and operations teams partnered to develop
The price optimization project has reinvigorated RM and deploy the price optimization solution to a global
at IHG. As we previously mentioned, the imple- hotel enterprise. To date, price optimization has gen-
mentation of targeted enhancements to the existing erated $145 million of incremental revenue for IHG
PERFORM system was an early outcome. Price opti- and its franchise partners.
mization also inspired a multimillion-dollar initiative IHG’s price optimization system is already having
to revamp HOLIDEX. A new central reservation sys- a major impact on the hotel industry. Other leading
tem, REVOLUTION, will streamline the definition of global hotel enterprises are currently developing their
rate products and ensure that a rational rate struc- own LOS price optimization solutions. Carlson Hotels
ture is in place at all hotels. The price optimization is implementing a staynight price optimization solu-
project also raised the visibility of the RM group’s tion (Rozell 2009). The methods for estimating price
forecasting expertise within the global IHG organiza- response developed at IHG are applicable to many
tion. The RM group is now considered the corporate industries. The specific problem of optimizing price
forecasting center of excellence, and the development for demand based on LOS is directly transportable to
of an enterprise-wide forecasting platform, predictive rental cars (length of rental) and airlines (origin and
demand intelligence (PDI), is a testament to that. PDI destination). The IHG experience also helped inspire
generates a forecast that integrates with the key cor- the development of similar methods to optimize the
porate business functions of finance and marketing price of juice drinks in a resource-constrained supply
and with property-based RM, including PERFORM. chain (Bippert 2009).
By using a common forecast, IHG is better able to align
marketing and budgeting with the tactical pricing and Appendix A. Optimization Model
inventory control RM processes. We deployed a PDI Formulation and Solution Methodology
prototype in the fourth quarter of 2010; the production
Our model is an adaptation to the hotel LOS prob-
system is currently under development. Also, because
lem that Gallego and van Ryzin (1997) proposed for
we deployed price optimization to a majority of prop-
the airline network problem. The plain formulation
erties, we can no longer measure the uplift of price
without business rules is listed below.
optimization using control properties; hence, a project
is underway to construct a performance measurement Variables
model, which will be similar to the model we devel- Rad : Rate for arrival date a and LOS d. (Optimiza-
oped during the initial research and scoping phase. tion decision variable.)Koushik, Higbie, and Eister: Retail Price Optimization at IHG
Interfaces 42(1), pp. 45–57, © 2012 INFORMS 57
Dad = f 4Rad 1 CRad 5: Demand generated for arrival and beta properties. Despite our best efforts to con-
date a; LOS d is a function of the hotel’s rates (Rad ) trol the variability, REVPAR changes were extremely
and competitor rates (CRad ). volatile. Therefore, we used a Pearson’s chi-squared
cos tad : Room turn cost for arrival date a and LOS d. test to test our hypothesis that price optimization
L: Set of all resources; resources are the stay dates properties outperformed their control group. If the
with available capacity. price optimization property REVPAR change was bet-
C4l5: Set of all arrival date and LOS combinations ter (i.e., a larger increase or smaller decrease) than that
consuming resource l. of a control property, we counted that treatment con-
Cl : Available capacity of stay date l. trol pair as a win for price optimization; otherwise, we
The contribution function that IHG’s price opti- counted it as a loss. Comparing the frequency of wins
mization optimizes follows. and losses, we computed a chi-squared test statistic
for the hypothesis with much more power than a sim-
X
Max contribution = Dad · 4Rad − cos tad 51 ple means test. For the beta properties, we observed
ad 41 wins and 27 losses, resulting in a confidence fac-
where Dad = f 4Rad 1 CRad 51 tor of 91 percent that the price optimization prop-
erties performed better than their control properties.
subject to The test of means showed a 2.7 percent improvement
X in REVPAR with a confidence of 80 percent that the
Dad ≤ Cl ∀l ∈ L improvement was greater than zero. Typically, a simu-
ad∈C4l5
lation methodology is used to measure the benefits for
Rad ≥ 0 ∀ a1 d0 such a capability (Smith et al. 1992). However, mea-
suring the benefits using a test versus control group
Because both demand and prices are decision of hotels is a reliable methodology. Because of the
variables, we implemented a special optimization methodology’s rigor in selecting control groups and
method using a decomposition heuristic that lever- the involvement of stakeholders in the benefits mea-
ages CPLEX. We believe the decomposition heuris- surement process, we were able to explain the benefits
tic is better than a Dantzig-Wolfe decomposition case to the senior executives.
approach or a dynamic programming approach
because the optimal prices in the near future must be
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