Pricing with Imperfect Information

Pricing theory is extremely compelling if we know what’s going on. The problem with applying the theory is that our information is so far from perfect, it may not even be relevant, accurate, timely, or available. Most often, we attempt to construct a model of our pricing universe with a few solid data points, a number of less precise figures, and a lot of good ol’ fashioned gut feel. Sometimes we get some fairly accurate numbers once per quarter, as a by-product of finance crunching numbers for Wall Street. I liken this to driving a car with a muddy windshield by looking in the rear-view mirror every five minutes. You can do it if you drive really slowly and don’t mind a lot of wrecks, but it’s not a good way to drive.

Pricing with imperfect information will be the subject of a couple of upcoming presentations I’ll be doing this year at pricing conferences. Naturally, this is a challenge we face constantly.

Along these lines, there was a great article in the New York Times (free registration required) about the Garden State Plaza mall. Because it’s so close to New York’s financial institutions, analysts visit the mall disproportionately to judge the financial health of leading retail chains. The retailers know that an analyst’s impression can lead to a buy or sell advisory, and substantial swings in the stock price. Therefore, they tend to send top employees to this mall and open new concept stores there. One of the most important tasks for the analysts is to assess the stores’ pricing strength. But how do you do that, before the stores announce results? The analysts look for subtle signals, like the size of “For Sale” signs. Large signs indicate desperation, small signs confidence. Some stores don’t even post signs, they just mark down prices right on the tags (a practice likely to be more prevalent when stores know analysts are trying to judge how desperate they are to put goods on sale). One analyst wondering why a store was discounting boxers in the holiday season, since they are “giftable.” (Perhaps they’re part of a loss-leading promotional strategy? Or perhaps the store is just giving away money? We don’t know.) Analysts also look for signs of excess inventory, and even more subjective factors such as the style of the season’s collection. (One hedge fund manager stopped buying Bebe because of the “godawful” colors.)

People inside the retailers should have better data, of course, and be able to make confident pricing decisions. Unfortunately, this is not always true. The data may exist, but that doesn’t means the people making pricing decisions have the information they need at their fingertips. We’ll be talking a lot more about how to handle less than perfect information in 2006…