Carlson Hotel’s decision to move forward with the SNAP program is the result of extensive research into existing revenue management (RM) systems and processes. Its research revealed that traditional solutions do not solve the right problem for today’s hospitality industry. The traditional approach to revenue management (RM) identifies opportunities to restrict product availability on busy nights, also known as yield management. In today’s market conditions hotels are experiencing fewer soldout nights, making yield management a sporadically effective lever at best. Carlson determined that our hoteliers needed to understand the price at which their hotels would make the most money on all future booking nights. This is a very different question, so it was determined that Carlson Hotels needed to take a radically different approach.
SNAP represents a totally new approach to revenue optimization that brings together the historically separate activities of pricing and revenue management. SNAP assesses current supply, demand and competitor rate data to determine optimal stay night prices for participating hotels. Through design and prototyping activities we also became aware of some significant opportunities in the way that RM solutions are usually delivered, so we took the opportunity to objectively revisit the technology.
Over the years many hotel companies and several vendors have linked RM to property management systems (PMS). From the technology perspective that makes a good deal of sense – robust PMS interfaces help operators obtain extract data and implement inventory controls, and reduce the risk in RM implementations. But ease of implementation is not the paramount consideration when enabling a revenue-generating function like revenue optimization.
The PMS is central to everything that goes on in a hotel operation, with multiple mission-critical operational processes and much of the training that hoteliers receive dependent on the technology. Reliability, stability and ease of use are the most fundamental requirements in a PMS technology. Revenue optimization, on the other hand, is concerned with understanding the market, identifying opportunities and enabling hoteliers to capitalize on them to maximize revenue. It seeks to predict changes in customer behavior and market conditions and be nimble enough to adapt to them.
When considering the user communities of both systems, it is clear that hotel operations management and revenue optimization require different skill sets. Most RM systems had their origins in the passenger air industry, where large communities of experienced analysts focus 100 percent on RM decisions. The skill set and process of an airline RM analyst’s job are different from that of a hotel operator who has to run a 24-hour guest-centric operation with multiple operational functions. Yet the hotel RM process and systems still work in a similar manner. Carlson found that even the simplest property-based RM systems still demand extensive interaction with analytical content, and this is time-consuming for time-pressed hotel operators.
The company felt we could redistribute this workload more effectively and relieve our hoteliers by deploying more flexible technology. It was partly for this reason that Carlson decided to implement enterprise forecasting first, and then progress to SNAP to automate an optimized pricing process for its hoteliers. This made it easy to focus deployment of the tools on the appropriate user communities, with a central forecasting process managed by expert resources feeding a simplified property-level process for managing prices.
Finally, and most importantly, we considered the possible risks of the RM solution to our technology investment. Carlson could have chosen an established solution that is relatively easy to implement; but current market conditions show how a shift in guest behavior can render traditional RM approaches ineffective. A significant drop in year-over-year demand restricts the yield management opportunities to even fewer stay nights, yet if we had invested in a standard RM approach we would still be in a situation to maintain and support a solution that adds little value to our hoteliers.
Conversely, imagine what would happen if we had an excellent RM process we were happy with, but we decided that we need to change our PMS. If the systems were highly interdependent, this demanding transition would be made more difficult because an additional major process change to another mission-critical function would be required. Carlson wanted to keep these processes as independent as possible, and worked to deliver a modular, enterprise solution rather than provide a one-size-fits-all RM technology to Carlson properties.
Good revenue optimization is about producing useful insights, not following processes, and when chains relegate RM to property system processes they miss important opportunities. When Carlson embarked on its revenue optimization technology program, the goal was to provide technology-enabled services that would enable its hotels to be as successful as possible. We found that by using powerful, flexible, centralized applications we can wrap a more effective support and service layer around RM.
For example, the current generation of hotel RM solutions places the task of managing the demand forecast in the hands of the individual hotelier. But why burden 1,000 separate hoteliers with the need to manage a demand forecasting tool when a hotel company can employ a team of experts to centrally manage forecasts for all hotels using an enterprise forecasting technology? Not only is the centralized approach far more cost effective, it also creates opportunities when critical demand data is made available to the other centralized functions, such as sales and marketing.
Carlson looked beyond hospitality technology to find examples of how enterprises leverage decision support. For example, companies who make and/or distribute physical products face multiple, frequent decisions that can have a profound effect on profitability. The mature discipline of supply chain management leverages centralized demand forecasting capabilities whose insights, i.e., demand forecasts, can inform better decisions across the enterprise. The same forecast that enables better production planning decisions, for example, can also drive better logistics decisions, and better pricing decisions for the same company. And by driving multiple decisions from a single view of demand, enterprises can leverage technology to eliminate inefficiencies. It seemed like a direction that would also work for the hospitality industry.
SNAP is the latest step in an overall revenue optimization strategy that focuses on delivering money-making insights to Carlson hoteliers. First, we established a central support team of revenue optimization experts, then we equipped with best-in-class decision-support technologies, and now we are convinced that we are placing the right tools in the hands of our hoteliers. This approach enables Carlson Hotels to develop deeper expertise, find innovative solutions to enterprise problems, and engage with innovative suppliers to identify and implement best-in-class solutions that benefits all of its hotels.
Paula Winkler is the EVP operations support services and CIO for Carlson Hotels Worldwide.