How analytics helped Ford turn its fortunes

Big data and analytics permeate virtually every move Ford makes, from forecasting the worldwide price of commodities to figuring out what exactly consumers want, what it will build, where it should source parts and how to power its lineup of vehicles.

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"Data will set you free." That's how CEO Alan Mulally has launched many a meeting since his 2007 arrival at what was then the sinking ship called Ford Motor Co.

Six years later, Mulally's opening line has become the automaker's bona fide company mantra. Bearing good news or bad, Ford managers wouldn't dream of coming to a meeting without hard metrics in hand.

"Historically, you didn't want to share bad news at Ford," says John Ginder, manager of system analytics and environmental sciences and a 22-year Ford veteran. "What Alan brought to the company is a mindset that we would no longer operate on anecdotal evidence. I've seen that explode in the past five or six years. Our CFO and COO are huge proponents. Today, it's all about having a data-driven perspective."

Indeed, data and analytics permeate every business move that Ford makes, from forecasting the price of commodities to figuring out what consumers want, what the company will build, where it should source parts and how to power its lineup of cars and trucks.

Crunching data behind the scenes are some 200 big data and analytics experts from a broad spectrum of disciplines. They work in what Ford calls analytics centers of excellence, which are found in various units of the $134 billion company, including marketing, research, credit services and others.

Since 2007, these analytics experts have contributed mightily to urgent strategic and tactical turnaround decisions, working on projects that ultimately decided issues such as which brands and models to discontinue, where to procure parts and materials, and how to enable dealers to tweak their inventories to improve sales.

Quarterly financial figures, Ford's stock performance and the company's most recent annual report tell the rest of the story so far. In 2009 -- one year after reporting a record $4.6 billion loss -- Ford posted a profit for the first time in four years. That same year, it launched 25 new vehicle lines and sold 2.3 million cars and trucks in the U.S., becoming the first brand to top the 2 million mark in U.S. sales since 2007. In October, the automaker reported its 17th consecutive profitable quarter.

Data analytics is a key competitive tool for all carmakers, which are slicing and dicing data on customers, production, vehicles and more to predict demand and hone their product offerings. General Motors, for example, has been collecting vehicle diagnostic data and other information via its OnStar system for years. But Ford is heavily focusing its analytic efforts on customer preferences, an area where it appears to be ahead of its competitors, according to Thilo Koslowski, an automotive analyst at Gartner.

"Everyone is working on [analytics] and to varying degrees have [gained] insights with regard to product usage," he says. "I see Ford having explored this sooner than others, especially in terms of understanding customer preferences."

At Ford, "there was very much a sense of urgency, and that tends to drive people to think outside of the box," says Ginder. "There's nothing like a crisis to help get the type of acceptance that's important [with analytics]. The crisis of 2007 and 2008 was a major turning point for our company and the role that analytics can play. With that combination of crisis, new management and a new perspective, we were emboldened and empowered to look at things quite differently."

Ford has derived some of the greatest returns from analytics investments focused on three areas: ascertaining what customers want, managing vehicle complexity and delivering to individual dealerships the right cars with precisely the right features that customers in that particular geographic area want to buy.

The Right Car to the Right Dealer

For decades, Ford, like all automakers, has relied on extensive market research, surveys and focus groups to get a grip on the heart's desires of drivers.

"But that doesn't always give us a complete picture because that data needs to be standardized in order to do comparisons," says Mike Cavaretta, project leader for predictive analytics at Ford. One way the company is addressing the issue is by monitoring social media for more specific intelligence and customer feedback.

"The nice thing about social media is that people elaborate," Cavaretta says. "They talk about more things and go beyond whether something is simply cool or not."

For example, when Ford was monitoring social media streams to learn how people felt about its three-blink option for signaling a lane change, the company learned a lot more about turn signals than it set out to know. "We found that in some vehicle lines the turn signal wasn't high enough or [was] in the wrong place. The problems people talked about weren't with the three blinks, but other aspects," Cavaretta notes. Ford was able to incorporate such feedback into decisions about new products and features.

Building in the features that drivers want is one thing. But ensuring that dealers have those cars on hand to sell is absolutely critical to turning a profit. "Quite a few customers walk into a dealership and want to leave with a vehicle that day, so we're limited to vehicles on hand that day," explains Ford research scientist Bryan Goodman, who works on analytic systems that support sales and marketing and their intersection with materials planning and logistics. "We have to get the right vehicle with the right engine and right set of features and controls to the right dealerships."

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