How analytics helped Ford turn its fortunes

Ford is using big data to drive virtually every aspect of its global turnaround.

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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."

Merging Data Lanes

To do that, Ford integrates and analyzes several data streams, including data on what it has already built and sold, data on what has sold in the context of what was available in inventory at the time of the sale, plus data on what customers are searching for and configuring on company websites. This data is then combined with economic data to predict vehicle sales relative to housing starts, employment rates and other information. The system is known as the Smart Inventory Management System, or SIMS.

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