J.D. Power Acquires We Predict Bolstering Auto Quality Analysis

Automakers and consumers closely watch results from the influential auto quality studies published each year by data and analytics company J.D. Power. Those studies, in part, are based on customer surveys. Responses can be subjective. Remember the year Hummer owners flagged as a quality problem the voracious fuel appetite of the gas-guzzlers they enthusiastically purchased?

J.D. Power has tweaked its methodology over the years and now the company is adding new, hard data to its toolbox. The company announced Monday it acquired the data and predictive analytics business of We Predict. The U.K.-based company’s software draws from billions of actual vehicle service records and uses machine learning and predictive analytics to develop detailed projections of future warranty claims and repair costs.

For instance, a We Predict study published last August revealed while initial repair costs for electric vehicles were much higher than those for internal combustion engine-power cars and trucks.

Specifically, the study found a year after an electric vehicle’s launch the number of so-called “incidents” per 1000 vehicles dropped 33% from its initial launch and repair costs declined 27%. But by the second year incidents per 1000 vehicles fell by 14% but the cost to repair those vehicles decreased 65%.

That data is becoming even more important as automakers introduce many new electric vehicles and EVs begin to attract more customers, especially in light of soaring fuel prices. It’s vital information that might not have been available from consumer surveys.

It’s a key point noted by Doug Betts, president of the global automotive division at J.D. Power.

“As the automobile industry enters a phase of massive transformation in which electric vehicles and ever-more complex technologies are rapidly becoming the norm, warranty claims and repair costs are a critical variable for manufacturers and suppliers to incorporate into their forecasting,” Betts said in a release announcing the We Predict acquisition. “By incorporating We Predict’s comprehensive data and powerful analytics into our vehicle quality, dependability and valuation platforms, we will be able to create the industry’s most robust and accurate view of future warranty claims and repair costs.”

Indeed, J.D. Power president and CEO Dave Habiger indicated adding We Predict’s access to billions of service records and computing capabilities to analyze all that data can only enhance his company’s offerings.

“By augmenting our existing offerings with We Predict’s forecasting software, we will be able to deliver a more complete, detailed view of repair-related costs to better anticipate financial risk exposures,” said Habiger in the acquisition release.

For We Predict, its acquisition by data giant J.D. Power may be construed as both a victory and validation. The much smaller company published its first public quality and repair studies last year. While not taking obtuse shots at J.D. Power’s methods, in interviews its leadership would suggest the company’s hard data, not colored by human perception or brand bias, was much more accurate than consumer surveys.

It’s a point not lost on We Predict CEO James Davies who noted in the release, “

“J.D. Power invented the idea of using data and analytics to evaluate vehicle quality and dependability, so the opportunity to become a part of that team and bring our software and operational data into the offering is enormously exciting to all of us at We Predict,” Davies said. “The industry and consumers need accurate repair cost forecasting now more than ever and we look forward to being the leader in delivering those solutions.”

Under the new arrangement Davies becomes vice president of repair analytics and data at J.D. Power. We Predict will become part of the global automotive division at J.D. Power.

It would be an easy bet to now predict future J.D. Power auto quality studies might augment subjective consumer responses with We Predict’s cold, hard data that holds no affection for brand or model.

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