Hotel Comp Set Analysis: A Little Knowledge

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Hotel Compete publishes an alternative view on current industry discussion.

We cannot understand comp set quality by analyzing only the competitors that hotels include in their sets – we must also understand which ones they left out.

Hotel Comp Set Analysis has been back in the news in recent weeks. It is an important topic, but industry coverage tends to focus on advice and best practice, rather than data-driven analysis. Hotel Compete has long regarded this as a missed analytical opportunity and a problem.

The findings of a new analysis of hotel comp sets were published recently in Hotel News Now. The article, “Comp sets raise RevPAR indexes,” centered on a study of some 30,000 comp sets and drew the following conclusions:
1.    The average Occupancy, ADR and RevPAR indices for all 30,000 hotels are close to 100%, “dispelling” the “myth” that hotels tend to pick comp sets which can be easily out-performed
2.    Hotels with two or more comp sets are more likely to have higher RevPAR indices
3.    The more often a hotel is named in somebody else’s comp set the better it tends to perform

Taken at face value, these are interesting findings. Their relevance to hotel performance analysis, however, is less clear. A more detailed discussion of these findings and their implications is available on the Hotel Compete website.

It’s always interesting to know what competitors are doing, and in this sense knowing the extent to which hotels name one another back in comp sets probably provides some sense of validation. But it still misses the key point of comp set selection. True comp sets are comprised of hotels that compete directly for business – or, more specifically, bookers.

It seems unlikely, then, that the secret of good comp set selection lies in the deeper understanding of what other hotels think. Bookers have no insight into the composition of hotels’ comp sets, nor the rules or the participation constraints that influence a hotel’s selection of competitors. Bookers choose from all of the substitutable alternatives in the market. A good comp set, then, provides a fair representation of a competitive market – for example: the hotels that are likeliest to be on the screen together when a booker is making their selection.

To evaluate hotel comp sets analysts need an objective way of defining each hotel’s direct competitors. Hotel Compete accomplishes this by using market data to build a statistical “booker’s eye view” of the market. The data-driven models have led to one critical realization: We cannot understand comp set quality by analyzing only the competitors that hotels include in their sets – we must also understand which ones they left out. This is a critical blind-spot in name-back analysis

This study has provided food for thought. An obvious extension of this analysis is to compare the competitors that hotels include in their comp sets to the competitors identified by a data-driven model. We will perform such an analysis and return with results. Watch this space!

  • Note: The article referenced above, “Comp sets raise RevPAR indexes”, appeared originally on

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