The Decline of Sears Is a Story About Narrow-Minded Analytics
I am a data-driven marketer, but I also talk about the dangers of using analytics for narrow-minded goals at the expense of long-term advantages. The story of Sears and its eventual bankruptcy is very illustrative what I mean about narrow-minded analytics — used for short-term gains at the expense of longer-term goals.
I know, because early in my career, I had spent several years at Sears. More importantly, I was there when Sears was bought out by Kmart holdings.
In 2004, Sears was already in decline. But it was still a force to be reckoned with. Despite the fact it had struggled to improve its soft lines (apparel, textiles, etc.) performance, it was still the go-to retailer for hard-line goods, such as appliances and tools. Management was also trying new formats and new product lines to rejuvenate the Sears brand.
Then the announcement came. Sears will be bought out by Kmart Holdings and ESL investments, run under the leadership of Eddie Lampert. The feeling among Sears employees was immediate demoralization. It was as if an old but proud ship was under attack by a ghost pirate ship under the flag of a cursed and dead brand.
Sensing the fear, senior management began preaching the benefits of a more efficient, data-driven management mindset that ESL investments would bring. Along with more resources, the data-driven culture would reward “smart risk-taking.” By better leveraging data, Sears would climb out of its slow descent to once again become a dominant leader in retail.
In this spirit, I became involved in an aftermarket pricing project, where we leveraged pricing and sales data to determine the optimal price of thousands of parts used in the repair and maintenance of hard-line goods. The project netted over $10 million in the first year alone, and the team was recognized with the “making money” award (Yes, that was the name of the award). As more price optimization projects came online, tens of millions of dollars in bottom-line revenue were being realized quarterly.
While the pricing initiatives were a brilliant use of analytics, senior leadership didn't take advantage of the analytical talent to address the issue of the declining top line. Where was the data-driven strategy for top-line growth? Were we simply collecting cash for the big transformation? Was something already in the works? As we tweaked and re-tweaked algorithms to squeeze more profits, the brand atrophied. Long story short, you have what Sears is today.
However, this story is not an indictment of the transformational powers of data-driven thinking. Rather, as I have written in previous articles, such as here and here, this is an indictment of management’s ability to exercise visionary, data-driven thinking. Analytics is a powerful tool, but it doesn't replace courage and visionary thinking.
Sears was so busy picking up loose change off the floor, it forgot to look up at the bus barreling toward it.
With analytics, this is easy to do, because it is exceptionally good at optimizing for your current environment. Changing the rules, however, requires the blend of analytics and courage.
Some argue that Eddie Lampert and ESL investments always planned to juice and kill the Sears brand. Eddie Lampert has denied this from the beginning. I believe him, because there was a time when Sears’ coterie of store brands (such as Kenmore and Craftsman) still carried immense market value. That was the time to begin stripping Sears.
This is simply a story where the potential and power of data-driven thinking was advertised as an opportunity for transformational change, but was frittered away picking up loose change.
Shiv Gupta is a principal at Quantum Sight Marketing LLC. He helps clients develop data, analytics and digital technology strategies to drive compelling relationships with customers. In this blog, he'll discuss ways in which marketing organizations can regain their strategic bearings and leverage their tech stack for both short-term and long-term gains. Reach him at firstname.lastname@example.org.