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CarHub.com Quiz Targets Super Bowl Viewers

CarHub.com today released results of a consumer survey designed to help manufacturers and marketers select which models to advertise during the upcoming Super Bowl.

by Staff
January 19, 2017
2 min to read


BEVERLY HILLS, Calif. — CarHub.com announced the results of a consumer survey that could help manufacturers and marketers select which models to advertise during the upcoming Super Bowl.

The ongoing study is being conducted in the form of CarTron, an online quiz designed to help car buyers decide which type, make and model of vehicle suits their personality and needs. More than 25,000 respondents had completed the survey as of press time.

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With the Super Bowl less than a month away, the quiz includes several sports-related questions. CarHub.com analysts have found that participants who identify themselves as sports fans tend to prefer “family-friendly” vehicles to more “sophisticated” models and are typically more interested in horsepower than fuel economy.

Sports fans also expressed a preference for happy endings and nonfiction literature and tended to admire Michael Jordan more than Mother Teresa. Among those who consider great professional athletes to be role models, the survey found a number of consistencies in selection, including:

  • More fun, less practical vehicle types

  • Models that are powerful and make an impression

  • Models that are more fun to drive than to look at

The list of vehicles recommended to sports fans has run the gamut from “family-friendly” (e.g. Honda Odyssey, Toyota Sienna, Subaru Outback) and “powerful” (Dodge Ram, Ford F-150, Dodge Charger) to “sophisticated” (Tesla Model S, Audi A4, Chrysler 300) and “eco-friendly” (Toyota Prius, Nissan Leaf, Chevy Volt).

“The same personality traits that lead us to define excellence in specific ways also lead us to define the ideal vehicle in similarly specific ways,” said Jesse Toprak, CarHub.com’s founder and CEO. “It is these connections that CarTron takes advantage of in its vehicle recommendation algorithms that leverage seemingly unrelated questions to predict vehicle preferences.”

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