Hospitality Revenue Management Solutions Buyer’s Guide

Lessons in Revenue

Revenue Optimization

In today’s competitive landscape, revenue optimization is paramount for maximizing financial outcomes in the hospitality industry. From forecasting demand to implementing strategic pricing, this ebook delves into the four core functions of revenue optimization: forecasting, optimizing, controlling, and monitoring. Learn how to harness these principles to drive increased revenue and profitability, ensuring your hospitality organization achieves its full potential.

Lessons in Revenue: Revenue Optimization eBook Cover
Revenue Optimization Table of Content 3LESSONS IN REVENUE: REVENUE OPTIMIZATION What Is Revenue Optimization? The word “optimize” is a popular buzzword in business today, and for good reason. Derived from the Latin word optimus, meaning “best,” optimization is the process of making something as good as it can be—a worthy goal for any ambitious company. For hotels—and by hotels we are referring to the full range of hospitality organizations that practice revenue management, from independent properties and hotel groups to resorts and outdoor accommodations—revenue optimization means aspiring to produce the best possible financial outcomes from available demand. The Automation of Optimization With so many moving parts in managing a hotel, it’s virtually impossible to optimize effectively without a revenue management system (RMS) to automate the process. An advanced RMS forecasts demand as many times a day as needed, updating room pricing and availability as market conditions change. The RMS handles the bulk of decisions, alerting the user when manual intervention may be required. The user can also step in to provide information the RMS doesn’t yet know about, such as a newly announced event, which could impact supply and demand in the area. This manage-by-exception approach removes the risks of manual processes: slow response times, human error, and dependency on individual expertise. Transitioning to an Integrated, Dynamic Approach Different RMS platforms take different approaches to optimization, generating different results. Traditionally, hotels have taken a linear approach, which tends to oversimplify assumptions about demand behavior and focuses primarily on pricing tactics. This can lead to misguided decisions and missed opportunities, preventing hotels from achieving their full revenue potential. More recently, advances in revenue technology coupled with access to more comprehensive data sets have enabled hotels to transition to a more integrated and dynamic approach to optimization. This approach recognizes the complexities of market demand, inventory management, and the hotel business mix. It goes beyond simple pricing tactics to leverage a broad range of controls, helping hotels achieve the best possible revenue outcomes from available demand. Forecasting a Complete Picture of Available Demand The linchpin of the optimization process is the demand forecast, which provides an estimate of demand over a future period. Importantly, the demand forecast projects unconstrained demand, or the amount of business the hotel could sell assuming their room capacity were unlimited. This is different from the occupancy forecast, which projects constrained demand, or expected occupancy. Rather than simply accept the first business that comes along, as many hotels are liable to do, the optimization process takes a more strategic approach. The RMS identifies the business mix within available demand that is likely to bring in the most revenue for the hotel. It then implements controls to capture that demand, closing out all other business. Building Greater Precision and Reliability into Forecasts The outputs of the optimization process are only as good as the inputs, and this is where oversimplified forecasting methods can skew results. If the RMS relies on limited or irrelevant data inputs to forecast demand, such as the on the books, the competitor’s entry pricing and regrets/ denials data, it will produce an incomplete or distorted picture of demand. This will throw off the entire optimization process, leading to poor revenue decisions. Advanced forecasting methods draw from a broader set of demand inputs. In addition to booking pace and competitor behavior, this includes historical patterns, market conditions, events, days to arrival, and the impact of pricing and availability controls. The RMS employs sophisticated algorithms to capture the complex relationships and interdependence among these inputs. Recognizing that demand can vary by room type, product type, and market segment, it forecasts demand at a more granular level. As a result, projections of available demand are more precise, accurate, and reliable. Uncertainty: The Only Certainty in Forecasting Another shortcoming of traditional forecasting methods is the tendency to take a deterministic outlook on future outcomes. This assumes that future events can be predicted with a high degree of certainty and that outcomes are determined solely by the input variables. However, hoteliers know well that the real world can be unpredictable and messy at times. Outcomes are difficult to predict due to the presence of unknown, random factors that may affect the accuracy of predictions. Uncertainty resides in everything from cancellation rates to economic conditions to weather patterns. Unexpected events can come at the micro or macro level and can strike suddenly or trickle in slowly. And the further away the day of arrival is, the greater the degree of uncertainty. When hotels don’t account for uncertainty in forecasting and optimization, they risk being caught unprepared if circumstances change unexpectedly. A good example is no-shows. It is virtually impossible to predict daily no-show rates because no-shows are random and unpredictable. Yet if a hotel doesn’t anticipate no-shows, it will find itself with unoccupied rooms on sold-out nights. Factoring Uncertainty into Revenue Decisions Advanced revenue optimization recognizes that forecasting is an imperfect process. Instead of assuming the forecast will be perfectly correct, the RMS takes a stochastic approach. This means acknowledging the inherent uncertainty in future market conditions and hotel performance and accounting for a margin of error in revenue decisions. Rather than predict precise outcomes, the RMS applies probabilistic models and statistical techniques to estimate the probability distribution of future events, producing a range of possible outcomes and factoring them into pricing strategies. This means avoiding aggressive pricing decisions when there is a possibility that demand has been overestimated—and vice versa. When an unexpected event does occur, the RMS tries to anticipate whether it’s a short-term event or a long-term trend, in part relying on user inputs. It then re-forecasts and reoptimizes to produce the most favorable outcomes under the circumstances. This enables the hotel to adapt quickly to changes in market conditions. 8LESSONS IN REVENUE: REVENUE OPTIMIZATION The Domino Effect of Pricing Changes Traditionally, revenue management systems have taken a linear approach to the optimization process. The RMS begins by producing the demand forecast, then identifies the optimal business mix, and then implements pricing and controls. However, this approach fails to account for a fundamental characteristic of demand behavior: when pricing changes, demand changes. The degree of change will vary according to price sensitivity and supply availability, and the relationship is not linear, as many pricing tools assume, but curved, with steep sides on either end. Generally, however, a change in the price of a product will impact demand for that product and may also impact demand for other products. Further, a change in the price of a product will also change the price of any linked products. It may also affect overbooking decisions, last room value, and which products the hotel should make available, and which should be closed out. All of these factors can change revenue outcomes, altering the original assumptions of the optimization. Recognizing Diversity in Market Segments An integrated approach to optimization is especially important given the complexities of the hotel business mix. Traditionally, the RMS would lump together rate codes and market segments, treating them as equals during the forecasting and optimization processes. However, rate codes sometimes behave very differently even though the PMS may categorize them within the same segment. For example, the negotiated corporate segment may include both clients with fixed rates and clients with dynamic rates tied to BAR rates. An increase in BAR rates will increase pricing for clients with flexible rates but will not affect pricing for clients with fixed rates. Similarly, availability controls may affect clients with yieldable rates but won’t affect clients with last-room availability clauses in their agreements. The Benefits of Behavioral Segmentation All types of products consume the same resources in terms of occupying hotel inventory. To find the mixture of controls that maximizes revenue from available inventory, the RMS must manage all of the demand together, factoring in the relationships and trade-offs among different products and market segments. A best-in-class RMS will break down traditional PMS market segments and rate codes and create new groupings based on shared demand behaviors. This means separating fixed and flexible rates, last-room-availability (LRA) and yieldable rates, and fenced and unfenced rates. By supporting a more robust forecasting and optimization process, behavioral segmentation produces better decisions and more accurate results. The Drawbacks of Over-Reliance on Rules-Based Pricing Most revenue management systems take a reactive, rules-based approach to pricing controls. As demand conditions change, such as an increase in occupancy or competitor rates, the RMS increases the room rate for the entry-level or base room type. Other room types are tied to the base room type, and pricing for these rooms is programmed to increase at the same time according to predetermined increments or percentages. This means that if a hotel increases BAR rates due to high demand for standard rooms, pricing for deluxe rooms and suites is pushed up too, even if demand for these categories is soft. This drives demand even lower for these room types and heightens imbalances in available room inventory. Dynamic Pricing by Room Type To optimize effectively, the RMS must recognize that different room types have different levels of demand. It must forecast demand for each room type, not just the entry-level category, and dynamically price each room type individually rather than automate pricing based on fixed offsets. Pricing can still follow a hierarchy, but the RMS will have more flexibility to price room types closer together or further apart based on demand and how competitors are positioning each of their rooms on key booking platforms.RULES Moving Beyond Pricing-Only Controls Another important task of the optimization process is to determine which rates to make available for which lengths of stay, and for which product or room. Traditional approaches focus on manipulating pricing to capture demand, paying insufficient attention to the role of availability controls in maximizing revenue. When availability controls are used, they are implemented sequentially, without accounting for the impact of one control on the other. Hotels often price rooms by day, taking each night as a standalone and making decisions for that night alone. Under this model, the RMS increases rates on peak nights, continuing to accept one-night stays even when there is demand for longer stays. Not only does the high rate deter multiple-night bookings, but once the peak night fills, the hotel will have to turn away all business, including valuable multiple- night stays, and will end up with lower occupancy than necessary on shoulder nights. The better strategy would be to implement availability controls to stop accepting one-night stays on the peak night and perhaps even lower the rate to attract longer stays that would fill the surrounding nights. Sometimes it’s necessary to sacrifice a portion of revenue on high-demand dates to grow revenue over the entire period. Pricing by Length of Stay An integrated approach to optimization means optimizing pricing and availability at the same time. The goal is to make the most of existing demand for all nights, not just nights of high demand. This means acknowledging that different guests arrive on different nights for different lengths of stay. The RMS must consider the full network of stays and the overlapping patterns for each arrival day and length of stay, by room type. To balance inventory and optimize revenue for the entire period, pricing should be based on length of stay, and availability controls should be implemented in tandem with pricing decisions. Optimizing Inventory Management Overbooking controls are another weakness of a simplified approach to optimization. Most revenue systems focus heavily on the hotel’s entry-level room type, which is often where most of the demand exists. Premium rooms are used as overflow, helping the hotel fill rooms in the short term but negatively impacting profitability over time. If proper booking controls aren’t in place, the hotel will stop selling standard rooms once they are sold out, missing out on booking opportunities. If demand is soft, the hotel should oversell standard rooms, planning to upgrade the overflow. On the other hand, if demand is strong for premium room types or price sensitivity is low, the hotel would be better to close out standard rooms to force sales of premium room types. Dynamic Overbooking and Upgrade Paths An advanced RMS will dynamically inflate overbooking for standard rooms to continue to take reservations while also not blocking people willing to pay extra for premium rooms. These are foundational tactics of revenue management that can have a significant impact on revenue, yet few RMS platforms offer this type of functionality. Advanced optimization folds all the hotel’s inventory into the process. It assumes that some of the demand, even if already on the books, will cancel or no show. And it combines dynamic room-type pricing with strategic upgrade paths to strike a healthier balance between overselling rooms and pricing higher-tier rooms correctly. This helps optimize inventory in ways that rules-based pricing tactics simply cannot. Additional Optimization Features Here are a few more examples of how advanced optimization breaks away from simplified assumptions and solves real-world problems in revenue management. Calculating last-room value (a.k.a. hurdle rates): Going beyond basic pricing and inventory controls, the RMS assigns a minimum acceptable rate, or last-room value (LRV), by room night and length of stay to ensure the hotel accepts only the most valuable demand across all arrival dates. Simulating outcomes: Before committing to a change, such as lowering rates or accepting a group booking, the hotel can test the impact of the change on the forecast and any ripple effect it may have. This helps prevent bad decisions that harm revenue outcomes. Factoring in channel contribution: The lowest, publicly available rate for the entry-level room type, on a specific booking domain (such as the hotel’s website or an online travel agent) is often used as the benchmark when determining price. This does not provide the full picture of the competitive landscape as other offers or products offered by competitors on more opaque channels such as the Global Distribution System may shift market share. Incorporating cancel-rebook behavior: Some OTAs and third-party sites monitor online rates on behalf of travelers then cancel and rebook the room if the price drops. The RMS takes such behavior into account when making pricing decisions. 15LESSONS IN REVENUE: REVENUE OPTIMIZATION Sub-segmenting demand: Most RMS tools use rudimentary calculations to forecast the number of adults and children per room. An advanced RMS provides more precise projections of these subsegments to improve revenue and operational decisions. Protecting inventory: If a hotel has high demand from low-rated business with last-room availability (e.g. corporate and loyalty rates) the RMS can implement controls at the hotel level to temporarily close availability, thereby preventing the hotels from filling with this business. Leveraging artificial intelligence: An RMS powered by artificial intelligence autocorrects as needed with each implementation, continuously learning about the property and adjusting models to produce the best results without the need for human intervention. How Often Should the Optimization Be Run? The timing of optimizations plays a key role in the results they achieve. When optimizations are run infrequently, they draw from outdated data and skew results. When optimizations are run frequently, business conditions are reevaluated regularly, and pricing and controls are updated to make the most of current conditions. Most RMSs run on a scheduled optimization—ideally, at least three or four times per day. However, an advanced RMS will also offer the option of on-demand optimization. On-Demand Optimization If demand conditions change suddenly, it may be appropriate to run an on-demand optimization rather than wait for the next scheduled optimization. For example, when a concert or conference is announced, or a large group cancels, or the booking pace is far off of expectations. The revenue manager should first make manual adjustments in the RMS if it hasn’t already picked up on the change. Then an on-demand optimization can be triggered to make sure the hotel is making the best decisions based on the new conditions. If the change is relatively minor, however, and is unlikely to result in changes to pricing and availability controls, the hotel may decide to wait until the next scheduled optimization. The end goal is to ensure the hotel responds to market changes more quickly than competitors and seizes opportunities to capture optimal market share. Moving Toward Dynamic Optimization The next big iteration in optimization is dynamic optimization. An advanced RMS platform will automatically detect significant changes in market conditions or internal demand in real-time and will self-trigger an optimization Moving from Revenue Optimization to Profit Optimization Another exciting innovation in RMS technology is the introduction of features that enable hotels to progress beyond room revenue-based optimization to total profit optimization. A best-in-class RMS provides insights into the total contribution and value of each guest, market segment, distribution channel, and room type. The RMS evaluates total revenue, including spend in on-property restaurants, retail outlets, and ancillary services, along with the costs associated with acquiring and servicing the guest’s stay. This allows the hotel to compare the relative costs of different types of business and target the most valuable demand, such as guests who are less expensive to acquire and service and guests with the most spending potential. When managed effectively, profit optimization can have a direct and powerful impact on the bottom line. PROFITREVENUE Convergence: Finding the Optimal Mix of Controls To summarize, here are the key features of an integrated and dynamic approach to optimization.  A fully automated process recognizes the real-world complexities of market demand, inventory management, and the hotel business mix.  The demand forecast draws from a broad set of inputs, capturing complex relationships and forecasting demand by room type, product type, and market segment to produce a comprehensive picture of available demand.  Recognizing the inherent uncertainty in future market conditions and hotel performance, the RMS optimizes for a range of possible outcomes and adapts quickly to unexpected changes.  Optimization is an integrated process that forecasts, optimizes, and sets controls simultaneously, incorporating the impacts of changes to pricing and availability controls into decision-making.  Market segments and rate codes are grouped based on demand behavior. All demand is managed together, recognizing the relationships and trade-offs among different products and market segments.  The RMS forecasts demand for each room type and dynamically prices each room type individually.  Rather than price rooms by day, the RMS prices rooms based on length of stay, balancing inventory and optimizing revenue for the entire demand period. The RMS applies a combination of pricing and availability controls, applying them in tandem to account for the impact of one on the other.  The RMS folds all the hotel’s inventory into the process, dynamically managing overbooking controls, anticipating cancellations and no-shows, and following strategic upgrade paths to maximize the revenue potential of room inventory.  Optimizations are run several times per day to ensure pricing and controls always reflect current conditions, with the option to run optimizations on demand or on auto-pilot.  Going beyond room revenue optimization, the RMS evaluates total revenue and costs for each guest, segment, and room type to capture the most profitable demand. When all these elements are combined, the RMS iterates until it reaches convergence—the mix of controls that, when applied to demand, produces the best revenue outcomes for the property overall. Glossary Average Daily Rate (ADR) Actual daily room revenue/total rooms sold. Best Available Rate (BAR) The lowest non-restricted rate bookable by all guests. This rate can change several times a week and up to several times a day. Booking Pace The speed at which bookings materialize over a period of time from the booking date to the arrival date. Booking pace is expressed as a fraction of bookings received on certain days in advance. Constrained Demand The number of rooms that could be sold considering the hotel’s capacity or restrictions on bookings. Last Room Value (LRV) / Hurdle Rate The maximum amount of room revenue a hotel can expect to make from the last room available for sale. The system uses LRV as a restriction control for low value rates during busy periods and opens all rates during slow times. Market Segment A portion of the customers who possess a common set of motivations as well as a combination of unique purchasing (e.g., advance purchase vs. walk-in) and usage patterns (e.g., single night vs. weekly). Online Travel Agency (OTA) Third-party internet sites consumers can use to book hotel, air, car rental or tour activities. This indirect channel charges a commission or marks up the rate to guests. Examples include Expedia, Booking.com and Ctrip. Optimization The use of forecast, inventory, rate, configuration, and user interaction to calculate the best pricing and inventory control decisions that maximizes quality revenues for a hotel. Optimal pricing, LRV, forecasts and overbooking are the end result of the optimization process. Revenue Optimization A business discipline and culture that focuses on balancing supply and demand in a rational and systematic way to maximize revenue and profit while managing risk under current and anticipated market conditions. Revenue per Available Room (RevPAR) Daily room revenue/total rooms available. For a comprehensive list of hotel revenue management terms, visit our Glossary on IDeaS.com. IDeaS, a SAS company, is the world’s leading provider of revenue management solutions and consulting services. Combining industry knowledge with innovative, data-analytics technology, IDeaS creates sophisticated yet simple ways to empower revenue leaders with precise, automated decisions they can trust. Discover greater profitability at ideas.com.

Wait! There’s so much more to discover.

© 2024 IDeaS.
Discover Greater Profitability
×