One of the most difficult concepts to explain in Mathematical Rate Setting is that the optimal rate is the one that has the best chance of maximizing revenue over the long term, not necessarily in the short term. In other words, it is the rate that has the best chance of delivering the optimal revenue if the same rate decision where repeated many times. That does not mean that it is the best rate for any one time that you make the same rate decision. Let’s say for next Tuesday you calculate mathematically that the optimal BAR rate should be $100. Since the reservation process is random you don’t know with certainty which shopper will be willing to pay more or which one thinks the rate is too expensive (there are ways to track this but most hotels don’t have the resources so let’s assume we don’t know). At your weekly strategy meeting, your GM, having done no analysis, overrules your rate decision and asks you to raise the rate to $120. His decision delivers the desired results, meaning that the property received as many bookings as you had forecast would be picked up at the $100. The GMs decision just make the property an extra $20 per room. Where you wrong?
What the GM does not known is that, based on the hotel’s historical booking patters, he got lucky. The guests that booked happened to be willing to take his rate. The next time he makes the same rate decision and the right shoppers don’t happen to show up, he will give back all his winnings and probably more. This is how casinos take your money in the long run. Your $100 rate, however, if determined mathematically, has the best chance of delivering a positive revenue impact over the long run. That is because it turns away the least number of shoppers which perceive the rate as too cheap while giving the smallest bargain to those who would have paid more. That is what it means for a rate to be optimal.
An easy way to visualize the concept of optimization is to think of a game of darts(see image below). The GM(black dots) has hit the target a couple of times but his shots are all over the place and are therefore inefficient. It looks like he got lucky those two times. The optimal shooter(yellow dots) never hits the bulls eye but the shots are close to each other, efficient, and therefore deliver a better overall score.
Therefore, even if the GMs “hunch-based” decisions happen to work out better on any given day, and your $100 is never exactly the best rate that should have been charged, since it promises to deliver the best long-term value, it is therefore the best decision. This idea of Expected Value is central to the science of Decision Making and it is at the core of the algorithms used in all RMS systems. To learn more, follow the link below.
