The arrival of price transparency and dynamic pricing in the travel and hospitality industries – fueled by the rise of the Internet and a dynamic hyper-informed consumer – demands a fresh approach to traditional Revenue Management (RM). This article explores how these disruptive changes resulted in Pricing and RM innovations that led to the rapid expansion of the discipline into industries such as automotive, cruise lines and retail. In addition, the article provides real-world examples of innovative RM strategies undertaken by companies such as InterContinental Hotels Group, which decided to make a giant leap from traditional yield management to price optimization, then continued to leverage predictive analytics coupled with RM capabilities.
‘Control your own destiny or someone else will’. Jack Welch
The discipline of Revenue Management (RM) rose to prominence in the 1980s and 1990s by offering and optimizing differentially priced, fenced products to various market segments to drive organic revenue growth. In the early days of the discipline, airlines were responding to the disruptive event of de-regulation, which led to fierce competition and reduced profits. Airlines developed initial RM capabilities, and the results were substantial, giving rise to in-house RM teams and rapid development of technology to deliver innovative strategies across travel and hospitality.
RM practitioners built organizations of analytically minded people and invested heavily in yield management systems to forecast demand and optimize the availability of these different products. Then came the Internet and the era of price transparency, the seismic shift that shook the foundations of yield management and sent shockwaves through the travel and hospitality industries.
This disruptive event – fueled by the exponential growth of the Internet and a win for consumers who could now easily and quickly compare prices for airline seats, hotel rooms, cruise prices, etc. – initially caused chaos in the C-suite. Consumers, for the first time, were empowered to almost instantly assess and value products before buying them. With price shopping and comparison across competitors facilitated by technology, companies began to focus more attention on pricing and made more frequent price changes. The result was fierce and dynamic price competition.
The arrival of price transparency and dynamic pricing demanded a fresh approach to traditional RM. This article explores how these disruptive changes resulted in Pricing and RM innovations that led to the rapid expansion of the discipline.
For hotels, the gains from optimizing room prices led to a broader application of advanced analytics. Soon, it wasn’t just room pricing that was being influenced by advanced analytics. Now hotels and cruise lines were using RM to set promotional pricing and to create product bundles.
Moreover, the concept of using analytics to optimize price positioning fueled the expansion of RM to different industries, including retail, automotive, media and entertainment. Today, the notion of using advanced analytics to optimize prices is nearly ubiquitous. Yet it all began with the disruptive event of price transparency and the courage of some early innovators. Here’s how RM got to where it stands today – and what the future holds.
PRICE TRANSPARENCY AND THE HYPER-INFORMED CONSUMER
The Internet’s facilitation of price shopping has transformed a number of industries. This technology, coupled with Amazon’s penetration pricing strategy, changed the way people buy books, movies and consumer electronics. The automotive industry is also evolving from one where the true price of a vehicle was opaque until negotiated, to one where auto dealerships generally now advertise their best pricing online in order to drive demand.
Among the first industries to face the challenge was travel and hospitality. The Internet spawned online travel companies like Expedia, Orbitz and Travelocity that made it easy for consumers to quickly compare prices and value for hotels, flights and cruises.
Prior to this shift, pricing in hotels and cruise lines had not been as dynamic. Most hospitality chains would have a ‘rack’ rate and a number of discount programs that were a percentage off of the rack rate. Yield management systems helped the hotels to optimize which discounts should be open when based on demand, but never provided any guidance as to whether the rates customers saw were optimal.
Then, in the 2000s, the number of Internet hotel booking channels skyrocketed. This innovative trend, combined with the deepening travel recession in 2001 and the tragic events of 9/11, put downward pressure on hotel revenues. Hotel occupancy rates fell by 15–20 per cent at leading hotel groups and US hotel profits fell by more than $600 million (Eister et al, 2012).
Suddenly, yield management wasn’t the answer anymore. That’s because the benefits of yield management models are derived from tightening inventory controls and establishing rate fences (restrictions) when demand is strong. But when demand is soft, inventory controls cannot advise on how low prices should go to improve demand.
With consumers having the ability to quickly line up options and sort by price, pricing became a much greater focus for hospitality companies. Hotels began buying data on competitor prices and assessing how they should be priced against competitors in order to drive demand and maximize revenue.
The largest hospitality chains created nextgeneration RM systems to optimize each hotel’s price positioning versus its competitors.
TAKING A LEAP FROM YIELD MANAGEMENT TO PRICE OPTIMIZATION
One of the innovators in next-generation RM was InterContinental Hotels Group (IHG), the global hotel operator that includes such wellknown brands as Holiday Inn, Holiday Inn Express, InterContinental, Crowne Plaza and Staybridge. Like other hotel firms, its use of RM in the 1980s and 1990s was based largely on room inventory and yield management of discount rates. Prices were set manually and did not change frequently, then RM systems were used to automatically open and close discount rates by length of stay. The pricing process was manual, but worked well through the end of the twentieth century. The decline in hotel demand in the recession of the early 2000s, coupled with the rise of Internet booking channels and price transparency, posed challenges to the long-time RM calculus and required a fresh strategy for hotel owners like IHG. They decided that instead of focusing solely on inventory allocation, it would be more impactful to add focus to pricing. But to implement a rational rate structure and execute on dynamic pricing, IHG and others would need the automated capability to optimize prices.
IHG took the leap by devising a plan to design an innovative price optimization module and incorporate it into its existing RM system. The hotel company collaborated with Revenue Analytics to craft a solution that estimated the theoretical benefits at 2.75–6 per cent of organic revenue growth (Eister et al, 2012).
A high-level design and live market test project was needed, and began in mid-2007 with a prototype that was deployed to 13 hotels. The prototype led to successful results and strong adoption at hotels, and IHG set out to build an enterprise price optimization capability that would optimize price position versus competitors.
The new system was a dynamic new RM capability that was able to:
- Forecast demand for room nights by using the existing demand forecast;
- Predict consumer response to rates relative to competitor rates by using market response modeling;
- Select the optimal room rate, which could be calculated daily by combining price sensitivity with the demand forecast and competitive rates, thereby maximizing the contribution margin;
- Provide alerts and reports to identity pricing opportunities;
- Go ‘shopping’ each night for the latest competitor prices.
The ability to conduct smart competitive rate shopping was another RM leap. The hotel company shopped each of its hotels’ top competitors daily (actually, each night). But rather than shop each competitor, each product, each day – which would be extremely costly – IHG developed a random, stratified sampling strategy that allowed it to recommend shops by blending future activity and historic booking patterns. So, if a product has a high booking activity, then the probability of that product being shopped is higher.
This price optimization module calculates complex math: It solves approximately 1000 integer programs per day for each property, and on average, rates are generated for each property six times per day, and solves four million linear programs each day.
The inventive pricing module registered immediate and dramatic results. A statistical test concluded with 99 per cent confidence that the test hotels outperformed their control properties, with mean improvement in REVPAR (total revenue divided by number of room nights available) of 3.2 per cent. Anecdotal feedback also was highly encouraging. A hotel manager in Louisville, Ky., reported that he experienced the highest revenue week ever A history of RM and the advent of next-generation RM © 2016 Macmillan Publishers Ltd. 1476-6930 Journal of Revenue and Pricing Management 1–6 3 aside from Kentucky Derby weekend (Eister et al, 2012).
This dynamic price optimization capability marked a significant advance over the previous RM system at IHG – and other companies, for that matter. Although IHG had for years employed a sophisticated RM system for inventory controls, its pricing decisions were largely manual. This was a significant feat considering there were more than 76 000 pricing decisions per hotel per year. Now, equipped with a more sophisticated price optimization system, pricing decisions were analytically driven, automated, and drove organic revenue growth.
PRECISION PRICE POSITIONING PROLIFERATES IN HOSPITALITY
Price optimization reinvigorated IHG and caused other hotels to take notice. At full rollout, IHG estimated that the innovative capabilities would generate about $400 million in organic revenue growth from higher rates and higher occupancy.
Starwood Hotels Resorts was another early adopter. The chain, which operates more than 1,200 properties under such brands as St. Regis, W, Westin and Sheraton, collaborated with Revenue Analytics to install an industryleading RM system capable of optimizing prices at every hotel generating the right price, for the right customer, at the right time. In 2009, John Peyton, SVP, North American Operations, Starwood Hotels Resorts, spoke at the Georgia Tech and Revenue Analytics Revenue Management Price Optimization Conference, and highlighted Starwood’s journey to transform its global RM capabilities (Peyton, 2009).
Each day, Starwood’s price optimization system reviews and analyzes 3.9 million alternatives and then makes about 350 000 optimal rate recommendations. The system predicts the number of rooms that will be sold as well as the per cent of future demand that will be captured based on current price positioning at each hotel.
This system was a response, in part, to today’s hyper-informed, Internet-fed consumers. Starwood adapted to them and generated hundreds of millions of dollars in organic revenue growth. Frits van Paasschen, who was Starwood’s CEO at the time, says, ‘Forecasting and pricing are fundamental building blocks to send consumers targeted offers’. ‘We know which hotels are trending below where we think they should be, who we should be targeting and what price the offer should be’, he says. ‘At Starwood, we learned how to react with agility to hyper-informed consumers and to embrace them as a vector for profitability’.
Other hospitality chains also have invested heavily in price optimization capabilities. Marriott International, for example, rolled out its Retail Pricing Optimizer (RPO) in 2011 in hopes of offering its hotels an exclusive application designed to enhance RM efficiency system-wide. RPO helps to determine optimal transient retail rates using an analytically driven, market-based methodology (Esposito, 2011).
Today, most hospitality companies have systematic capabilities to guide hotels in optimizing price position versus competitors. This innovation, and the resulting financial benefits, have led many hospitality companies to invest in additional pricing and RM capabilities that help them optimize other rates and drive more effective promotions. RM teams are also getting involved with designing product bundles that will appeal to today’s hyper-informed consumers.
OTHER INDUSTRIES ADOPT AND EVOLVE PRECISION PRICE POSITIONING
Seeing the success of hotels, other industries were quick to adopt the concept of using analytics to optimize price positioning versus competitors. An early and natural fit came with the cruise industry, which was quick to realize the power of price optimization and swiftly implemented it.
For example, one global cruise line launched a dynamic RM system in order to forecast demand and compute optimize price position against competitors for various types of accommodations. The system generated a 2.8 per cent revenue uplift for the cruise line, as measured in a live market test.
Soon, industries with business themes similar to cruise lines – such as entertainment – plunged into precision price positioning. Multiple entertainment companies, including movie theater chains, theme park operators and media companies, have built sophisticated analytics to measure how consumers respond to various price positions and optimize price positioning across a variety of products. For many entertainment companies, these tools now drive ticket prices, as well as food and beverage prices. These capabilities are generating revenue bene- fits of 3–4 per cent for impacted products.
The spread has continued into the retail industry, where price transparency has had a massive impact with the growth of online retailers like Amazon.com. One major big-box retailer has implemented precision price positioning as a way to determine when, where, and by how much to respond to aggressive pricing actions by e-commerce competitors. Using advanced analytics to measure how consumers respond to price positioning against various competitors, the retailer created a breakthrough pricing framework to guide pricing decisions. This framework serves merchants who own pricing with an innovative system to present pricing data and recommendations along with easy-to-use dashboards, reports and timesensitive alerts.
An initial market test of these fresh capabilities showed an average 6.8 per cent revenue uplift and, on select product lines, the uplift exceeded 30 per cent. Leveraging predictive pricing analytics then flowed into other types of retailers. The retail automotive industry has also experienced the disruptive force of price transparency as car shopping has shifted online.
This sea change, which empowered car-buyers with more information than they had possessed before, has changed the car buying experience immensely. Sonic Automotive recently introduced a newvehicle pricing strategy that relies on proprietary software that helps set new-vehicle prices that are optimized for each local market. The prices are flexible, but the goal is to drastically narrow the gap between asking price and actual sale price (Harris, 2011). Fueled by advanced analytics, this innovative strategy serves two purposes: it allows Sonic to advertise a lower price to sell more cars which drives more store traffic, and improves the consumer car buying experience by reducing haggling.
Today, almost any organization can use RM processes and analytics to predict the impact of competitive pricing actions and react quickly to provide customers with the right product, at the right time, and for the right price. Doing this effectively drives organic revenue growth and allows companies to control their own destiny.
CONSOLIDATING GAINS AND FINDING THE NEXT FRONTIER
The continued growth of e-commerce and the widespread availability of information through the Internet has ushered in a fresh era of price transparency that disrupted a number of industries, from travel and hospitality to automotive to retail. Savvy companies responded by using RM principles and predictive analytics to develop dynamic capabilities to optimize their competitive price positioning. While the initial disruption brought fear and uncertainty, these innovative capabilities have unveiled value for companies that adapted, driving revenue gains of 3–5 per cent on average with analytics.
So what’s next? While the next generation of RM has yet to be defined, it will likely further embrace the hyper-informed consumer by creating more customer-centric offerings. By identifying what an individual wants and offering it to them at a time they are likely to be receptive, firms can grow revenue, share and customer loyalty. Many travel and hospitality companies are already devising innovative dynamic bundling A history of RM and the advent of next-generation capabilities that leverage analytics to find the optimal balance between purchase probability and company profitability, then present the most relevant offer to the customer.
What will be the next disruptive event that prompts a sea change in RM? For now, travel and hospitality companies are holding their own against disruptors like Airbnb, the online marketplace where people list and book accommodations around the world. However, the company already boasts of 1.5 million listings and more than 40 million guests, and is less than a decade old. Whatever the disruption is, history has taught us that RM principles will give the top practitioners the wherewithal to respond and thrive in any environment.
Eister, D., Koushik, D. and Higbie, J. (2012) Retail Price Optimization at InterContinental Hotels Group. Informs 42(1): 45–57.
Esposito, L. (2011) Revenue management helps Marriott weather recession’s aftermath. Hotel Business August: 20.
Harris, D. (2011) Sonic rolls out market-pricing plan. Retrieved from http://www.autonews.com/article/20110228/RETAIL07/ 302289973/sonic-rolls-out-market-pricing-plan, 28 February.
Peyton, J. (2009) Mobilizing Global Resources to Transform the Revenue Management Discipline. Speech presented at the Revenue Management and Price Optimization Conference, Atlanta, GA