Economists will tell you that for sellers, price discrimination is the best way to maximize profits. If a seller can charge more to buyers with a higher “willingness to pay” (i.e. have a higher valuation) of a product, and less to others, then margins as well as quantity will essentially be maximized. This isn’t a win-win situation though. The high valuation buyers are the losers, since in a non-price discrimination regime, where everyone gets charged the same price, they would pay less. Low valuation buyers win, because without the price discrimination regime favoring them, they would not have bought the item since it would have been too expensive.
In practice, no company knows valuations with precision. The companies who may lay claim to the best and most comprehensive consumer data are internet retailing companies like Amazon.com (NASDAQ:AMZN), which got in trouble for price discrimination in 2000 and promised not to do it in the future. Travel industry companies are widely suspected of price discrimination, for one. Customer loyalty programs, like frequent flyer programs, are one form of price discrimination which appears to be fully legal.
For investors, a company’s use of consumer data can be key. CVS (NYSE:CVS) appears to have the most advanced marketing and coupon system among basic pharmaceutical goods retailers. This system (along with better executive-level management) appears to be a major reason why CVS has way outperformed competitors Walgreens (NYSE:WAG) and Rite Aid (NYSE:RAD) since 2004. Given the vast amount of consumer data which exists, it is surprising that these systems are not better utilized by more companies. Regarding this broader trend, I recommend an interesting book by Yale Law professor Ian Ayres called Super Crunchers which goes over a number of different examples showcasing the use of statistics to inform everyday decision-making.
Former UC Berkeley professor and Google’s (NASDAQ:GOOG) current Chief Economist Hal Varian has now been saying for years that data-driven marketing and pricing are the wave of the future for businesses, and that the sexiest job in the coming decade will be statistician. One super-investor who seems to appreciate the power of accurately estimating customer risk with statistics is Bruce Berkowitz of the Fairholme Fund (FAIRX), to judge by his position in AmeriCredit (ACF).
Berkowitz began investing heavily in ACF during Q1 2008, along with Ian Cumming of Leucadia (NYSE:LUK). AmeriCredit provides financing to low-income individuals, often with bad credit, to purchase cars. Their risk analytics team is supposed to be the best in the business, meaning that their estimates of whether buyers will have trouble re-paying loans are more on target than others, which is obviously a tremendous advantage in providing correctly-priced loans to a low-income population. They also package these loans into Asset-Backed Securities (ABS). Bruce has done quite well with his position in ACF and other equipment rental companies as well, such as URI and HTZ, presumably because the economic downturn hurt their utilization rates (and stock price) badly but the recovery has been good to them.
A big drawback to price discrimination in a retail setting is that it makes people angry, and some aspects of it may be open to legal challenge. In a recent survey, 87 percent of people strongly object to online retailers charging different prices to customers for the same product. It is difficult at this point to see how it could lead to private class action litigation on behalf of consumers or involvement by the Federal Trade Commission (FTC), which generally focuses on anti-competitive practices such as price-fixing, or unfair or deceptive sales practices. Price discrimination on its face is legal, though if some customers are charged higher prices due to their race or gender, that is illegal.
Even with these concerns in mind, investors would do well in the coming years to pay attention to which companies in a given industry have the lead in consumer analytics. With more information and data available, the company which is able to make use of it the best may often end up with a better bottom line.
Disclosure: Long FAIRX and LUK.