Arity Announces the World’s Largest Driving Behavior Database for Targeted Marketing

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Mobility data and analytics company Arity announces Core Driving Risk Audiences made available to marketers via leading demand-side advertising and marketing data platforms. Marketers can now target messages to drivers based on their predictive driving risk, helping marketers optimize acquisition and profitability goals, while providing consumers more relevant offers based on their driving segment.

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Leveraging insights from nearly 100 million connections, Arity Core Driving Risk Audiences creates driver segments that allow marketers to provide relevant and meaningful offers to consumers based on telematics data. Leveraging insights from more than a decade of driving data history matched with insurance claims and losses, segments are calculated by proprietary risk algorithms that predict the risk of loss.

Coming at a time when personal transportation patterns have experienced unprecedented change, Arity’s Core Driving Risk Audiences empower insurance marketers to know upfront they are confidently reaching their most ideal customer audiences, as the lowest risk segment of drivers who have up to 5x higher Customer Lifetime Value than average drivers.

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“Traditional consumer marketing methods are limited, as they’re based on assumptions about demographics and shopping behavior, not tied to the best probability of a driver’s risk of insurance loss or policyholder profitability,” said Fred Dimesa, Director of Advertising and Data Solutions at Arity. “This creates inefficiencies for marketers as well as inequities with how individuals are exposed to opportunities. Arity can cut out the guessing game and ensure you spend acquisition marketing investment on your most ideal customers.”

Arity believes in using data for good. That means being a leader in the responsible use of data. To enhance privacy, Arity maintains driving behavior information in a de-identified format, not stored or associated with personal information like name and address, or any other information that could be traced back to an individual user.

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