Over the past 25 years, Revenue Management (RM) has grown from a new idea in hospitality into a core competency that is essential to any hotel’s success. In recent years, the internet has made pricing transparent and social media has empowered consumers. These dynamics have resulted in Revenue Management rapidly evolving from a function of managing demand to playing a critical role in generating demand. At the corporate level, hotel chains are investing heavily in new Revenue Management analytics to inform pricing, promotions and marketing. At the hotel level, Revenue Managers are pricing more dynamically across multiple market segments to drive demand and maximize revenue. It is an era of big data, and new Revenue Management capabilities are turning data into dollars.
In the late 1980s, hoteliers began to recognize an opportunity from applying the yield management techniques that airlines had used to drive revenue gains following airline deregulation. The airlines had focused on using data and analytics to forecast demand for different fare products, then optimizing inventory availability. They developed yield management systems that maximized revenue by precisely determining how many seats on each flight to protect for late-booking, high-fare travelers, and how many seats to offer at discounted fares to more price sensitive leisure travelers with more flexible travel plans. Following a chance discussion between Robert Crandall, then CEO of American Airlines, and J.W. “Bill” Marriott, Marriott International became an early pioneer of yield management in hospitality. These early efforts resulted in revenue gains of $150 million to $200 million for Marriott.
As a result of this success and similar success with other hotel chains, Revenue Management became a core discipline in hospitality. Similar to the airline practices from which it had been adapted, Revenue Management focused on managing inventory availability to maximize revenue based on expected demand. Revenue Management systems, some of the earliest “big data” analytics, crunched data for every reservation for the last two years to produce demand forecasts for every night for the next year, across various room types, seasons and days-of-week. For a hotel chain with 3,000 hotels, that means the Revenue Management system produces over 40 million new forecasts each night! These forecasts feed an optimization program that generates inventory controls for each hotel to manage the availability of different rates across various lengths-of-stay and to set overbooking levels.
When demand is strong, using analytics to yield manage demand to “cherry-pick” the highest value bookings is extremely effective. However, over the past 10 years, as pricing became more transparent and consumers were empowered by the wealth of information available to them through the internet, the fundamental Revenue Management problem for hospitality began to change. These changing dynamics also highlighted key differences between hotels and airlines that drove hospitality Revenue Management to evolve. First, hotels sell to a variety of market segments that can have different demand patterns and pricing models. Second, hotels have far more differentiated products than airlines. They can offer luxury, full ervice, limited service or economy products, each with various room types and amenities. They are differentiated from competitors by brand, quality, and, most importantly, location. Based on these different market segments and differentiated products, hotels fundamentally have more pricing power than airlines, whose products are often viewed as commodities.
Limitations of Traditional Revenue Management
During the recessions of the early 2000s and 2008-09, the environment of soft demand forced Revenue Managers to shift their focus from demand management to demand generation. With little demand to “cherry-pick,” their focus shifted from managing inventory controls to getting the right price to the right market segment to drive demand. Another factor that shifted attention to pricing was the new era of pricing transparency to consumers. Third party intermediaries (TPIs) like Travelocity and Expedia made it easy for consumers to compare rates across a number of hotels in their desired destination. When combined with more descriptive content online and easy access to user reviews through social media, consumers were empowered to make quick price / value trade-offs in deciding where to stay. This new transparency made it critical for Revenue Managers to closely monitor their price position vs. their competitors. In dynamic markets, hotels began changing rates multiple times per day based on their most recent data on demand and competitive rates.
The Power of Pricing
Today, pricing is one of the most critical decisions hotels make, and it is a core function of a new age for Revenue Management. Pricing is a key element in creating demand. Group pricing is highly competitive, and corporate / negotiated pricing drives room night production for key accounts. Transient retail pricing influences competitive positioning, customer value perception and search result placement. Price is also a key driver of RevPAR and profitability – a $1 reduction in ADR for a 500-room hotel with 70% occupancy would cost that hotel over $127,000 in lost profit per year. As a result, it is imperative, now more than ever, that hotels price with precision to balance market share and profitability.
The pricing problem is incredibly complex. For the typical hotel, pricing the next 365 days and setting rates for 3 different segments across 10 different room types for 7 lengths-of-stay requires 76,650 pricing decisions to be made every day! And each must consider a variety of factors, including demand forecasts, competitor rates, season, day-of-week, the price elasticity of demand and guest satisfaction. Given its importance and complexity, it is no surprise that the major hospitality brands and the RM professionals in hotels have made pricing a central focus of their Revenue Management efforts.
Pricing Precision with Price Optimization
Over the past six years, leading hotel brands have developed proprietary price optimization capabilities for deployment to all of their hotels through their Revenue Management systems. Our firm, Revenue Analytics, has worked with 3 of the 7 top hotel chains in the world to create custom price optimization capabilities. These capabilities are brand-specific with respect to the rates optimized, room types impacted and analytics deployed, but they are all based on the core concept of optimizing rates based on demand, competitor prices and the level of price sensitivity. With over 9,000 hotels with $50 billion in revenue utilizing price optimization capabilities worldwide, this pricing science is rapidly becoming industry-standard.
Price optimization capabilities mirror the way hotels think about pricing, adding automation and sophistication to drive precision and profits. The demand forecast is a key input. If a hotel only has a few rooms left to sell, it will have pricing power even if competitors are discounting rates. If demand is lagging, the hotel may be able to lower its price position vs. its competitors to take market share. The demand forecast needs to be at a low level of detail to account for demand for different rate products and room types, as well as length-of-stay.
The second major input for price optimization is competitor rates. It is important for hotels not to consider their own rates in a vacuum. Customers generally view a hotel’s rates in comparison with those of other hotels, particularly when shopping through a TPI. So, a competitor lowering rates can change a hotel’s price positioning, even if that hotel has not changed its own rate. Our research and analysis has shown that across various hotel segments, from luxury to economy, customers are more sensitive to a hotel’s price position vs. its competitors than the hotel’s rate alone. Craig Eister, Senior Vice-President of Global Revenue Management and Systems for IHG, who pioneered price optimization, has noted that “it’s not just about pricing, but market positioning.”
The final major input to price optimization ties the first two inputs together. Any pricing decision must factor in the notion of price sensitivity. By lowering price, how much more demand or occupancy does one expect to generate? By raising rates, how much demand does one expect to lose? The relationship between price positioning and demand is easy to grasp, but difficult to measure. The difficulty is due to all of the other factors that can drive demand, such as seasonality, day-of-week differences and market dynamics. We have worked with our clients to develop advanced market response models to account for these types of non-price demand drivers and measure the impact of price positioning on demand for each individual hotel. The resulting price elasticity results give insight into price sensitivity, and can be used in price optimization models to predict the impact of pricing actions on demand.
Using demand forecasts, competitor rates and price sensitivity, price optimization systems simulate multiple price-demand scenarios and recommend the optimal retail rates that maximize revenue. The systems present the recommendations alongside the key input data to ensure transparency to RM personnel. Then Revenue Managers review and approve or modify the rate recommendations and submit final pricing actions for the system to execute. Daily and on-demand optimizations assure that the right price for each room is always in the market.
These new pricing analytics have had a strong top line impact for hotels. In their 2009 annual report, IHG cited a 2.7% increase in RevPAR attributable directly to Price Optimization. That is a $400 million revenue impact across all IHG hotels. The IHG capability, which was an industry-first, was a finalist for the prestigious Edelman Award for innovation in Operations Research and Management Science. Marriott International rolled out their Retail Pricing Optimizer (RPO) in 2011, and reported multiple success stories to Hotel Business magazine. Russell Vereb, Marriott’s VP, Revenue Management Systems, told a story of a Marriott in San Juan, Puerto Rico: “Through RPO, it was recommended they drop their rate. In just two weeks, the property saw a significant increase in demand and after three weeks, it was seeing RevPAR growth.”
Integration with Marketing
Just as technological advances are bringing analytics to pricing, new data and technology are driving Revenue Management to better integrate with Marketing to bring science to other demand-mix decisions. Revenue Management can leverage its demand insights to identify need dates for promotions and optimize promotional offers, packages and price points. Revenue Management functions are integrating their demand forecasting capabilities into the promotions planning process. These forecasts can quickly identify need dates and time periods for promotions to fill rooms that might otherwise go empty. Equally important, such Revenue Management forecasts spot periods of high demand where promotional discounts would be dilutive, thus assuring higher ADR where it is attainable. These integrated Marketing / Revenue Management discussions are typically accomplished at a hotel level through coordination and discussion at weekly revenue meetings. At a corporate level, Revenue Management departments are aggregating demand forecasting data to assess demand across brands, markets and regions to garner market intelligence.
These demand insights are now informing promotions planning and other enterprise-wide decisions. This involvement is giving Revenue Management a more strategic role in demand creation. Craig Eister at IHG explains: “At IHG, Revenue Management is becoming more integrated in the heart of our strategic decision making. The key insights generated from our state-of-the-art forecasting and pricing technologies are being used to drive decisions such as where to invest marketing dollars, how to position our Sales accounts, and how to optimize distribution.”
Customer Lifetime Value
In addition to these demand insights, Revenue Management is beginning to mine guest spending and loyalty program data to better understand guest preferences and customer lifetime value. Understanding the preferences of loyalty program members and their responsiveness to packages and offers helps optimize packages and promotions. This analysis ranges from determining whether loyalty guests respond better to points offers vs. free night offers to measuring the likelihood that a guest who booked a suite also will purchase breakfast. These insights help structure future offers to generate the greatest lift per promotional or discount dollar spent. In addition, guest behavior and loyalty program data provide insights into stay patterns and spending over time, which facilitates calculating customer lifetime value. This greater focus on customer lifetime value is also an evolution for Revenue Management, which sprouted from the desire to maximize the profitability of each transaction. The advent of better data and analytics is helping to evolve RM thinking to maximize profitability over time by seeking to quickly identify, attract and retain high value customers.
Attracting and retaining high value customers is a strategic objective, and one that Marketing groups have emphasized for many years. Marketing organizations have also been developing analytics capabilities to drive these objectives, so combining their insights with insights from Revenue Management can be powerful. At Marriott, Revenue Management is now part of a broader Revenue Strategy and Consumer Insight team, led by Senior Vice President Dave Roberts. This team, says Roberts, “is working closely with the Sales, Marketing, eCommerce and Brand organizations, using analytics to support decision making across many areas, including sales deployment, pricing products, personalization and brand positioning.” In leading hotel companies, Revenue Management has evolved from a niche function managing inventory controls to become the enterprise forecasting and customer behavior modeling center of excellence.
Personalized Revenue Management
This emphasis on personalization is a new frontier in Revenue Management. By using technology to analyze spending data at an individual guest level, Revenue Management can stimulate future demand by reaching out to past guests with personalized packages and offers. Examples include offering breakfast in a package to a guest who often purchases breakfast, or mining from folio data that the guest stayed on a special occasion like an anniversary, and offering a discounted suite for the same weekend the following year. In the past, mining these insights would take significant manual time and effort, and might not be worthwhile. But as more data is stored and analytics engines become more robust, personalized analysis can be conducted across a large volume of guests at a time. As this analysis becomes scalable, it is more impactful in demand generation.
In addition, personalized RM can improve customer experience to help deliver more stays from high value guests. Hotels are developing capabilities to permit their highest value loyalty guests to book around minimum length-of-stay restrictions and reserve particular rooms. Opportunities exist to use mobile apps to recommend food and beverages, spa treatments and other experiences to guests based on what “guests like you” did during their stays. These capabilities improve guest experience and increase brand loyalty and preference. Based on its potential impact for demand generation and customer experience, the hoteliers who win in personalization will have a competitive advantage, according to Craig Eister of IHG: “This is the next generation of Revenue Management, where understanding the guest will allow us to win in this digital age.”
This era of empowered consumers and pricing transparency has forced Revenue Management in hospitality to evolve from a simple application of airline yield management to a more holistic function that is more critical than ever to hotels. The pace of this evolution is increasing as new capabilities like price optimization proliferate across the industry and drive RevPAR growth, and leading hospitality firms develop ever-broadening suites of Revenue Management analytics to optimize and personalize promotions, packages and guest experience. This rapid innovation is expanding the frontier of Revenue Management science and making the RM role more strategic. In this era of big data and competing on analytics, hoteliers who innovate and execute in Revenue Management will dominate those who fall behind.