Can Wal-Mart Get Any Bigger?

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In China's three main cities, according to a McKinsey study, increasing wealth will support 250 Supercenters among the competing retailers, each selling $24 million to $36 million annually. That's good. But a U.S. Supercenter sells four times as much.

Walking into a Wal-Mart Supercenter in Fort Worth, Texas, CEO Scott recalls that when Wal-Mart was an underdog, "you could really go after a competitor." Now the company no longer shows comparison-shopping baskets to demonstrate that Wal-Mart has lower prices than competitors. "It just looks like we're picking on people," he says.

To be sure, Wal-Mart has to keep finding new people to pick on. Over the past two years, Kmart filed for bankruptcy, and Ames and Bradlees, once East Coast powerhouses, closed up shop. Wal-Mart is quickly adding scalps in the grocery industry too, the venerable Grand Union among them. Safeway, Albertsons and SuperValu have all slashed their earnings estimates in the past few weeks.

Before getting into groceries, starting in 1986, Wal-Mart figured that a typical store needed a potential customer base of at least 150,000 people. But add groceries, and more of the available shoppers show up; each store needs a smaller area to support it. So Wal-Mart can situate Supercenters less than 5 miles apart in many suburban areas. It is also deploying a cut-down grocery-convenience store called the Neighborhood Market between the superstores. At the same time, Wal-Mart is adding merchandise categories, such as gasoline, Linux computers and flat-screen TVs, in which it can take prices down significantly. There's no escape.

Although Wal-Mart's stores may look identical, the company is pinning some of its growth prospects on the idea that what goes into them won't be. Wal-Mart's next competitive weapon is advanced data mining, which it will use to forecast, replenish and merchandise on a micro scale. By analyzing years' worth of sales data--and then cranking in variables such as the weather and school schedules--the system could predict the optimal number of cases of Gatorade, in what flavors and sizes, a store in Laredo, Texas, should have on hand the Friday before Labor Day. Then, if the weather forecast suddenly called for temperatures 5 hotter than last year, the delivery truck would automatically show up with more.

The company calls the program the "store of the community." The principle is as old as shopping: customers differ significantly depending on where they live, what they earn and other factors. But the differences are far subtler than anyone ever imagined. The company has been analyzing every purchase made over the past 10 years, looking at the relationships between the items people buy and hundreds of other variables such as time of day and price. The data miners are constantly searching for exploitable relationships--say, between sales of cameras and atlases. Consider: a slow-selling line of chicken pieces was slated for discontinuation at Sam's Clubs. But the software noticed that the customers who did buy the product were huge spenders on other merchandise. So the item wasn't necessarily a loser if it helped keep those customers coming.