Data Analytics Grows Sears Appliance-Repair Unit

The division is expecting 5% to 6% growth by eliminating 450,000 unproductive house calls.
David McCannApril 22, 2015

There hasn’t been a lot of good news about Sears Holdings in recent years, but a divisional finance chief for the owner of Sears and Kmart showed up at a CFO conference last month to present a success story about its use of data analytics.

One thing the company still has going for it is bulk. It’s been shrinking recently, but 2014 revenue still topped $31 billion. That equates to a large mountain of customer data available for analysis.

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Sears TruckAs a stand-alone company, Sears Home Appliances and Services would be comfortably within the Fortune 500 itself, with revenue of $8 billion. Brian Kaner, who spoke at CFO Rising East in Miami, came aboard as the unit’s CFO at the beginning of last year from Stanley Black & Decker, where he’d been a divisional CFO and business-unit president since 2006.

A key challenge for Kaner from the outset at Sears was to wring out cost and elevate productivity within the division’s $3 billion home services operation, which he said performs 8 million of the 22 million appliance repairs that take place annually in the United States.

Sears’ appliance-repair trucks carry about 400 parts, which Kaner said is sufficient to complete 70% of repairs on the first visit to a home. The 30% of service requests that take multiple visits to complete may sound like a lot, but according to Kaner it’s an industry-wide issue. Even to reach a 75% first-time success rate would require carrying 8,000 parts, he said, and 175,000 parts would be needed to complete all repairs the first time out.

Kaner set in motion a plan to use data analysis as a key tool to accomplish a goal of completing 95% of repair calls within five days of the service request. “We found that there actually was a way to get there, but not by determining what 400 parts are always going to be needed,” he told the crowd of finance executives.

Rather, the company developed “cases,” or repair scenarios, each accompanied by a set of questions a call-center operator would pose to the person calling about a malfunctioning appliance. Kaner’s division used the data from its 8 million calls per year to predict that, say, “95% of the time when somebody calls about a particular item, this is the part that’s needed.”

The final key was to then send that part to the person’s home in advance of the service call. “The way you grow a service business is to free up capacity and not run unproductive calls, saving that capacity to run the next productive call,” he said. “In our case that growth was roughly 450,000 calls. So we could grow the business by 5% to 6% just by taking out the unproductive pieces.” The division hasn’t yet eliminated that many calls but expects to based on its data analysis.

Kaner offered some advice for his peers about making data analysis useful.

First, get help. Hire a data scientist or a consultant to work with people within the company to build the needed IT infrastructure, organize the available data, determine what data are and aren’t useful, and test algorithms to see if the analytics is predicting the right answers. “You can’t just take mountains of this information and start building pivot tables to do all this stuff,” he said. Sears used a consultant and, given the value that was generated, is now looking to hire an on-staff person.

He also noted that it’s important to build a culture of “test and learn” where people feel like it’s OK to fail. “It’s a different mentality,” Kaner said. “Risk taking is now much more informed, but it’s also in much smaller bites. The risks we’re taking are not calculated, all-in risks. You test something in a certain marketplace, then you iterate.”

And, he counseled, be patient. “CFOs like things to be done overnight, and we’re very impatient with data,” he said. “We’re used to geeking around in our spreadsheets and thinking that we can download a bit of information and give it to you in minutes. But to do this right takes a fair amount of time. Not properly planning can lead to a lot of wasted effort and time.”

Photo: Random Retail, CC BY 2.0. The image is unaltered from the original.