Eaton Taps the Power of Prescriptive Analytics

By Samuel Greengard  |  Posted 2016-01-20 Email Print this article Print
 
 
 
 
 
 
 
Prescriptive Analytics

This multinational power management solutions company turned to analytics to get detailed information about market segments, pricing, branding and customers.

Today's business and market conditions are evolving at a rapid pace, so it's essential to understand the behavior and requirements of the marketplace. As a result, a growing number of organizations are turning to increasingly sophisticated analytics tools to deliver insights into how to run a business smarter and better.

One organization at the vanguard of this trend is the Hydraulics Group at Eaton, a leading producer of electronic controls, connectors, fittings, hoses and more.

The demand for these solutions, which are used across a wide range of industries in 175 countries, is somewhat dependent on the economy and specific industry conditions. Moreover, Eaton, like many companies, must deal with the nuances and intricacies of the market, the different geographies and dramatically different customer needs.

"In the past, we did a lot of cost-plus type of price bidding, and then we would go into heavy negotiation mode," recalls Matt Mehlbrech, vice president of IT for Eaton's Hydraulics Group. "Things would drag out, and our win rates weren't all that great."

The company recognized a need to migrate to a data-driven approach that could provide detailed information about market segments, pricing, branding and changing business conditions. "We knew we had to speed up and improve the sales process in order to gain wins and maximize sales and profits," he explains.

Turning to Prescriptive Sales Analytics

As a result, the company turned to prescriptive-selling analytics provider Zilliant to introduce a previously unimaginable level of insight. Eaton began using the vendor's MarginMax tool in April 2015.

"It provides a level of data science and fact-based information that allows us to get the price right from the beginning," Mehlbrech reports.

By plugging in past pricing, transaction histories, win/loss information, supply chain data, point-of-sale data (POS) and other factors, "We are able to build specific pricing models—and bands—that are dependent on the specific product segment and customer segment we are dealing with at any given moment," Mehlbrech explains.

For example, this means that the price of a hydraulic pump sold in Chicago may be different from the same pump sold in Seattle or Miami, and it may also vary based on supply and demand and other factors. "It's a far more dynamic model, and we can adjust on the fly," he says. "Because the pricing is more intelligent from the start, there is less need for negotiation."

The result? The Hydraulics Group has witnessed its sales conversion rates zoom from single digits to above 40 percent. The analytics solution also has helped sales teams tune into the specific needs of customers. Finally, Eaton can now deliver quotes to customers in about half the time it previously took: typically five days instead of 10.

The firm initially established pilot projects to test and demonstrate the power of the technology. "Once people saw what the system could do and how well it worked, they were completely sold on it," Mehlbrech says.

"We are no longer flying blind and guessing. We have deep visibility into the business. We're now in a position where we can use this analytics approach for the rest of the business."



 
 
 
 
Samuel Greengard writes about business and technology for Baseline, CIO Insight and other publications. His most recent book is The Internet of Things (MIT Press, 2015).
 
 
 
 
 
 

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