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Product tells who's likely to own particular vehicles

A new product lets dealers and the rest of the auto industry enhance marketing campaigns by selecting households with a high probability of owning a particular type of vehicle. Originally designed for use in states where motor vehicle registrations were unavailable by law, RL Polk & Co.'s Garage Predictors is a series of 129 statistical models that rank households by their likelihood of owning a certain

A new product lets dealers and the rest of the auto industry enhance marketing campaigns by selecting households with a high probability of owning a particular type of vehicle.

Originally designed for use in states where motor vehicle registrations were unavailable by law, RL Polk & Co.'s Garage Predictors is a series of 129 statistical models that rank households by their likelihood of owning a certain kind of vehicle.

The 1997 Driver's Privacy Protection Act and the subsequent Congressional Shelby Amendment have made vehicle registrations off limits to marketers.

“We simply created a product that models vehicle ownership based on statistics,” says Stephen R. Polk, president and CEO of RL Polk & Co. “It takes a look at a local market area and is based on a probability of vehicle ownership at a household instead of direct knowledge.”

Mr. Polk says its accuracy ranges from 50% on down, “which is certainly better than no knowledge at all.”

Users of the new product may select from several model categories including vehicle manufacturer, type, model year and market value. The statistical models are based on multiple sources of data, including household demographics and distributions of vehicles within neighborhoods.

Polk says Garage Predictors can be combined with demographic and lifestyle data to further enhance market selections or to statistically model trade areas.

Mr. Polk says Garage Predictors is an improvement over the product that previously was used in states where registration information wasn't available.

“We've taken a lot of the expertise we've developed over the years, running it off our 129 statistical models to rank each given household with the probability of owning a given type of vehicle,” he says. “It looks at probability down to a fairly small geography.”

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