Hotel giant Marriott International’s shares slipped 8% when the company came out with a tepid forecast for the rest of the year that lowered the “top end of its 2011 outlook by 2 cents to a range of $1.35 to $1.43 per share.” The company’s basis for a bleaker than before outlook was stated to be a relative inability to raise “rates on groups traveling to do business.” As a non-sequitur (given the downward revision applies to the near term) the company offered that “market tends to recover more slowly after a recession because its rates can be set years in advance“.
Stock analysts aren’t much better at anticipating either pricing power or earnings growth as a McKinsey & Company report from Spring 2010 year pointed out that “analysts typically lag behind events in revising their forecasts to reflect new economic conditions.” The consulting company’s study observed that ” when economic growth accelerates, the size of the forecast error declines; when economic growth slows it increases. So as economic growth cycles go up and down, the actual earnings S&P 500 companies report occasionally coincide with the analysts’ forecasts.”
Marriott’s recent pessimism may be a sobering counter to analysts’ expectations that the industry is out of the woods as well as a recognition of the fact that continued upward revisions of rates will likely drive customers down the price chain into more “affordable” segments of the industry. But ascertaining or, more accurately, estimating customer intentions is an imperfect art at best and arguably a mug’s game as a survey by the UK private jet company PrivateFly suggests that 27% of CEOs indicated that they would increase their travel.
One way of obtaining better probabilities for pricing outcomes for the near term could be to take a leaf out of a new book from the fashion industry, the use of crowdsourcing to predict what products will be a success or failure before they are even released. It involves the use of a web service, Krush which enables consumers and brands to interface directly thereby enabling financially better outcomes. Testing what customers could want to pay for a menu of service and facility options in destination markets could lead to better pricing decisions for hospitality companies most of whom currently adopt algortithms based on a smorgasbord of data much of which is rear-view mirror dependant and subject to the caveat that crowdsourcing and prediction markets work best for the near term, preferably no more than two or three quarters.
