Sunday, September 10, 2006

CarMax Does Data Better

The September 2006 issue of Business 2.0 has an article, “The Wal-Mart of Used Cars,” about CarMax, an analytics-driven chain of superstores for used cars.

In the same way that Wal-Mart revolutionized the logistics of retailing, CarMax set out to nail the perfect mix of inventory and pricing through exhaustive analysis of sales data. Its homegrown software helps CarMax determine which models to sell and when consumer demand is shifting. Each car is fitted with an RFID tag to track how long it sits and when a test-drive occurs....

Without the data, stocking CarMax lots would be a logistical nightmare. Each store carries 300 to 500 cars at any given time, and unlike Wal-Mart, the company has no vendors to stock its “shelves.” Instead, CarMax depends on 800 car buyers, who draw on the company’s reams of data to appraise vehicles.

The article doesn’t mention it, but I suspect that CarMax’s situation is one where the analytics appear to be the competitive advantage yet the real advantage is the data feeding the analytics. That is, analyzing sales and inventory data a la CarMax involves a mature set of techniques and tools; it’s highly unlikely that CarMax has found a new analytics secret sauce. Far likelier is that CarMax collects more and better data than the competition, allowing those mature analytical techniques to yield better results.

For example, consider two big advantages CarMax has in data collection:

  • It’s a network of superstores, each of which carries many more cars than a typical dealership. This scale means CarMax can sample the marketplace better than other used-car dealers.
  • CarMax’s car buyers act as a data-normalizing force, ensuring that the details of cars in CarMax’s database are classified in a complete and consistent way. This advantage is key compared to the obvious alternative of scraping eBay and other online sources of used cars, which together would comprise a sample even better than CarMax’s. The problem is, the greater quantity of data comes at the cost of much lower quality. There  would be no common definition of key attributes like “good condition” or, for that matter, no standards for what attributes to include. That means noisy, messy data—just the thing to make otherwise good analytics look bad.

So let CarMax be a reminder: Amid all the attention Internet-based businesses get for their unprecedented data opportunities, traditional businesses like used-car lots can be networked and data-intensified to compete in new ways as well.

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