J.D. Power acquires vehicle cost analytics firm We Predict

J.D. Power acquires vehicle cost analytics firm We Predict

J.D. Power has acquired U.K.-based automotive analytics company We Predict, the data analytics and customer intelligence company announced Monday.

We Predict’s software uses machine learning and predictive analytics to assess vehicle ownership costs as well as predict warranty claims and repair costs. J.D. Power said it will utilize this to “enhance its vehicle quality and dependability analytics, expand repair cost forecasting and provide critical valuation data.”

In an interview with Automotive News, Doug Betts, president of the global automotive division at J.D. Power, said his company will combine its customer data on vehicle quality with We Predict’s objective data from dealerships to help accurately predict vehicle quality and dependability.

“What [We Predict] has is actual objective data, like data from thousands of dealerships that says, ‘Here’s a car, here’s what broke, here’s what got fixed,’ ” Betts said. “So if you think about putting those two things together, and especially with [We Predict’s] data and the software that the company has created … you can then apply advanced mathematics to that and can forecast failure rates and do a lot of very interesting things for the automotive industry.”

Betts said J.D. Power hoped to use We Predict’s software to work on an EV index to gather data on metrics for electric vehicles that the automotive industry isn’t as familiar with.

“We Predict Data is very important to understanding what is ‘normal’ for an EV,” Betts said. “[With] all these companies now making EVs, what is the normal failure rate for a battery control system or for a battery or for a drive motor or all of these components that nobody’s really familiar with?”

We Predict CEO James Davies becomes vice president of repair analytics and data at J.D. Power, with We Predict being incorporated into the global automotive division.

“There probably isn’t anywhere else to take We Predict where the additive effects of the other data sets can be greater. [J.D. Power’s] got some really cool stuff,” Davies told Automotive News.

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