Using CompSets is fine for benchmarking but not for making pricing decisions.
In an age of increasing online bookings, hotel revenue managers too often get caught up in maintaining parity with their CompSet. Many still operate using that old school revenue management approach that relies heavily on their Smith Travel Research Competitive Set analysis.
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As a tool and gauge, use the STR CompSet for a quick benchmark. Fine. But please don’t use it as a basis for a pricing or marketing strategy to individual consumers. Why?
“Primary data driven strategies were better at optimising revenue 80% of the time.”
CompSet prices are a particularly mysterious form of alchemy. They are the epitome of secondary (external) data and can be driven by myriad factors that will always be unknown to you – how many rooms a property has left to sell, how the property is being perceived in the market, how they have done so far this year, how the revenue manager is feeling that morning, how lucky they are feeling…to mention a few. It’s arbitrary, at best. Those CompSets themselves are being driven by irrelevant factors like employee emotions and outdated RM systems that are not fit for purpose. That’s what we call, in academic parlance, sh*t data.
You, a prudent and sensible revenue manager, have no idea what ingredients are going into this sorcery. Is it a good idea to adjust your prices based off of competitors’ erroneously concocted prices? We argue that it is not.
We make the case that, in contrast to using secondary data, your primary data (all your internal hotel data sources e.g. transactions) is all that you need to plug in. Your own internal data tells you exactly how much demand you have, how much you need and at what prices demand is being realised. Ultimately, this is all you should want to know in order to maximise your revenue.
Pace, now FLYR for Hospitality, doesn’t look at competition to take pricing decisions – most others do. Here is why we are right.
Clean vs. noisy data
It is a statistical principle that the data that is the closest driver of a variable is the data that will always produce the cleanest measurement of that variable. This will, in turn, lead to better decision making. The further removed the data that you use is from the variable you actually want to measure, the worse your measurement will be. More factors add noise, which is not good for decision making.