Big break: Showing Toyota Financial Services how data, probabilities and analytics could dramatically improve its performance

Nate Litton knows that most of the people outside his team probably don’t fully understand what his job as chief data and analytics officer for Toyota Financial Services entails or how it works, but that’s OK. 

It’s only really important that he understand it, and that he can pass along what his team has discovered to those looking to make more informed decisions.

“I know a lot of the stuff that we’re doing is complex; there’s a lot of math and science behind it,” Litton said. “But we really do need to translate that in ways that others can understand, and understand what it means for their business or their areas.”

The Houston native has a math degree from Cornell University and master’s and doctoral degrees in statistics from Texas A&M University. 

“When I joined [Toyota], it was probably a little bit of an experiment,” Litton recalled. Toyota Financial Services had been missing its residual forecasts by a significant amount, and Scott Cook, then the company’s chief risk officer, thought that better data and analytics might close the gap. 

“Historically, what we had done in this space was just kind of hire car people, and they go ‘kick the tires’ to set the residuals, to tell us what it’s going to be worth,” Litton said. “Fast forward six months after I joined, and we had built a really high-quality forecasting tool to forecast residual values. The folks working on it didn’t really know a lot about cars, but they did know a lot about data, and we knew a lot about analytics.”

The proof was in the results, and at Toyota, those results got noticed. Soon, Litton was fielding requests for data analysis from other parts of Toyota Motor North America, and the famously analytical automaker was more fully embracing data analysis across a wide spread of its operations: sales and marketing as well as financial services. He also was building a team, which now numbers around 600 people, all of whom help better inform decision-making at the automaker through math and data analysis.

Take marketing, for example. “Ideally, what you’d like to be able to do is to incentivize and target people that would be on the fence about buying your product versus people who would buy it regardless,” Litton said. “So if you can figure out through data how to how to target more effectively, we can be more efficient with our large marketing and incentive spend going forward.”

— Larry P. Vellequette