MaS™: Models as a Solution
OWL combines a library of pre-built analytics algorithms to build customized RM models. The number of algorithms employed depends on the complexity of the RM problem at the property, the quality of the historical data, the revenue goals and the specific branding strategy. For example, a small, boutique hotel, without significant outlets, will only require an handful of algorithms to create a robust model. Conversely, a full resort hotel will require the breadth of our analytics functions. We leverage the latest advances in model deployment technology, so that you pay only for what you need. This intelligent approach makes our models economically scalable to any size hotel.
Why not Software?
Every hotel should have a unique rate strategy and the tools that you use to help set those rates should be built around that strategy. Gone are the days where “one-size-fits-all” vendors were the only ones supplying RM tools. Data access and computational power have become so inexpensive that creating a customized pricing model is now the best option for most hotels.
At OWL, we believe that standard RM software systems gets you to a level playing field – as level as any other hotel competitor that uses the same software. This tool may be acceptable for hotel companies with a conservative growth outlook or those wishing to standardize their RM practices regardless of each individual hotel strategy. However, these systems should not be the choice for those hotel companies who have an aggressive growth strategy or who need to reposition themselves or are the challenger brand in their market or are located in an unique market e.g. Independent Hotels. Conversely, these hotels need a more dynamic optimization solution that can be quickly tailored to changing market dynamics and innovative management tactics. Read more about RADAR™,GRO™, GEM™.
Experienced-based vs. Model-based RM Decisions
Let’s use Rate-Setting as our example. When making a rate decision, our brain is performing a series of mathematical exercises. Basically, our brain looks for patterns that, based on our experience, either signal an opportunity or a problem. Then we compute what the best response should be. Sometimes we raise the rate, sometimes we lower it, and other times we do nothing. Unfortunately, our brain has two limitations that make it quite inefficient for this type of complex decision making.
First, it is almost impossible for us to separate our biases from facts. For example, if we are very enthusiastic about the future, we will probably raise rates, even when the facts are telling us that we should probably make another choice.
Second, our brains can only calculate problems with a few variables. Rate decisions involve dozens of variables like market, guest behavior, booking pace, channel, room type, occupancy, booking window, competitor’s rates, season, and weekday. In addition, we have to look at these variables in relation to current and historical performance. A hotel with only 50 standard rooms and 50 superior rooms, requires millions of complex calculations on a daily basis to set the “best, most optimal” rate.