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 of what I mean about narrow-minded analytics — used for short-term gains at the expense of longer-term goals.

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.

But Your Data Is Fine, Trust Me …

Data … that great big, hairy gorilla in marketing departments all across the globe. We have Legacy Data, Subscriber Data, Third-Party Data, Business Data, Personal Data, Master Data, Sales Data, Reference Data, Privacy Data, etc., etc., ad nauseum. Now, during the last few years, the latest and greatest—Big Data and its cousin SoMoBi (SocialMobileBig) data have entered the fray enough to make everyone’s head spin.

Data … that great big, hairy gorilla in marketing departments all across the globe. We have Legacy Data, Subscriber Data, Third-Party Data, Business Data, Personal Data, Master Data, Sales Data, Reference Data, Privacy Data, etc., etc., ad nauseum. Now, during the last few years, the latest and greatest—Big Data and its cousin SoMoBi (SocialMobileBig) data have entered the fray enough to make everyone’s head spin.

No matter what you want to call it though, it just boils down to simple information. Information all you marketers crave. Information about your customer, your prospects, your products, your competitors and the trends that will steer you to hitting those numbers in the next and future fiscal quarters.

There is just so much of it, you say? No one here knows what to do with it, I hear? Every department controls a piece of it and refuses to share, is the excuse?

Maybe true. But, with a little time, effort and—of course—some of those ever-scarce budget dollars, you can create an environment where the grain can be separated from the chaff to build a healthy and robust universal silo of data which will benefit and streamline the efforts of every area of your organization efficiently and profitably.

There is no cookie-cutter data model for the business needs of every organization, despite the host of plug-and-play database tools and marketing automation processes available today. The information that makes your business research and marketing program successful is likely to be much different from what works for even your closest competitor.

At the core, your primary contact data for customers and prospects needs to be acquired and maintained as strictly as possible. My good friend, Bernice Grossman, along with fellow direct marketing legend Ruth Stevens, have a whitepaper I always refer to when providing guidance to anyone striving to establish or reorganize the variety of information that quickly begins to accumulate from different sources, in multiple disparate formats. Written as a guide for B-to-B organizations, the reasons and methodologies hold true for B-to-C. Even with the changes in data availability and the explosive growth of social data availability in the industry during the last few years, the white paper addresses the core data requirements for contact and communication.

Outside of the core basics of data needed to contact, track and segment your data pool, determining exactly what it is that gives you the edge is Priority One in deciding what else you must have available to make decisions. In every conversation or discovery session around data and database design within a CRM, the persistent desire that comes up is wanting a “full 360-degree view of my customers.” While that is possible with simply the basic contact information you have as the core of your data, along with whatever historical transactions available to provide RFM, most users expect a much deeper dive. At the more extreme illustration of designing your data around the optimal user experience, you have this infographic from Visual.ly that has been making the social media rounds. While extensive, the many comments on the sites where it has been posted point to even more data sources being needed to be all-encompassing.

If you, and your business goals, are like most, your time and budget is more likely going to place your need somewhere between the most basic and the most extravagant of these two extremes.

Discovering your own sweet spot is where the best value proposition is to create and maintain profitability for your business. That is where I hope to focus in the posts that will follow on a regular basis. I will be sharing points of interest, ideas, solutions and strategies for identifying the most accurate and efficient steps to take in planning the housing and process flow of all the data you need for success … with a dose of irreverence sprinkled in liberally along the way.