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Comment: The (mis-) use of statistics.

Benjamin Disraeli, the 19th century British prime minister is often quoted to have said: "There are three kinds of lies: lies, damned lies, and statistics." Others say it was American writer Mark Twain who coined it, since he liked it a lot. Whoever it was, I know what they mean.

Since my business of hospitality consulting depends a lot on finding and interpreting data, this is a subject that I have thought about. The relative value of figures perhaps comes most clearly into perspective if one takes a step back from a negotiation table at which the parties are hotly debating the fees to be charged by a hotel operator to an owner. Imagine the scene. "X percent!" shouts one party. "That's outrageous, it's got to be Y!" counters the other. "What about a sliding scale from Y to X, depending on the achieved GOP percentage?" a conciliatory consultant helpfully suggests.

All of them are missing the point, which is, a percentage of what? Arguing over percentages is often just haggling for haggling's sake. It's a principle inherited from village markets around the world, where you say "10" and I say "one" (dirham, riyal, rupee or whatever, for a dozen eggs).

The idea is that they're selling something and that somehow through cunning, strategy, nerve or simple force of character, you can brow-beat them into selling it for a song. It reflects well on the person who walks away from the market having seen the other party seriously lose out on the price of a horse, a cow or a camel. I read once about a horse market in Eastern Europe where the traders began by shaking hands and were not allowed to let go until the deal was done. I've seen a few hotel contract negotiations that looked like that.

But the point is, people are often prepared to argue passionately about a percentage of revenues or profits, when in fact they have no clue what actual dollar amounts are involved. It would certainly make a big difference to the way some contracts are negotiated, if both parties had a ready reckoner at hand (or on screen) to show the amounts in real millions of dollars that were implied by a half percent reduction or increase in management fees, for example. I don't know why nobody does that.

And then there is the data we search for most diligently when doing a feasibility study. The theory is that if you are testing a new hotel project, you should investigate how the hotels near your site are doing. Firstly you have to ask yourself what would be a reasonable 'comp set' for the proposed hotel. That in itself is open to many interpretations.

A standard comp set typically comprises five or six similar, nearby hotels, regardless of how many hundred there happen to be in the city. In reality, someone booking a Dubai hotel from the comfort of their computer in Germany, say, will cast a much wider net than most comp sets. That's why many hotels have several comp sets, say, a primary comp set and even three or four secondary comp sets. It's another example of an artificial data construct that can be thoroughly misleading if not thought through in depth, and even dangerous if the management contract happens to have a performance test that measures your hotel against the comp set.

When we finally obtain those golden nuggets of competitive data, from top-line KPIs like ADR and occupancy down to intimate details of beverage costs of sale or salary package components, we consultants exult in our perspicacity and omniscience!

But even if we were to be given a full 10-year copy of the entire accounts of each comp set hotel, would that really mean that our client could set up and run a new hotel in the same way? There are just so many differences between one hotel and another in concept, facilities, services, positioning, branding, etc, that the answer is almost certainly no. Let's be realistic: the best we can ever do is a well-informed estimate. But through seeing those pesky data in their true perspective, the best consultants can get very close to reality in their forecasts.

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Publication:Hotelier Middle East
Date:Jul 30, 2014
Words:717
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