Michael Lewis’ book Moneyball tells the story of how a major league baseball team used data analytics (aka “sabermetrics”) to gain a statistical advantage over competitors by challenging old and often subjective performance measurements.

The parallels in retail real estate are unsurprisingly similar.

As an example, two common data points found in property brochures include traffic counts and 1/3/5 mile radius demographics. While methods vary, some traffic counts are only updated once every 3 years. Further, demographics are often sourced from the U.S. Census which is updated once every 10 years.

This static data is supposed to help prospective retail tenants evaluate the market potential of having a presence in that location. And when ‘gut instinct’ enters the equation, reality can be further distorted.

By contrast, forward-thinking operators who leverage shopper analytics can provide a dynamic view of ongoing activity including peak/off-peak times, foot traffic flows and average visit duration. This provides practical data for prospects to incorporate into their analysis, often reducing location risk for both tenant and landlord.

Like sabermetrics in Moneyball, shopper analytics provides operators with a distinct statistical advantage over competitors…and better odds of surviving until the next US Census.