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Diagnosis: Data Addiction

A Business’ Most Vital Parts Can’t be Measured

By Mike Dea

Data is the most prized asset in business today. Whether it’s Google’s collection of data to optimize advertising algorithms, or how Equifax failed to protect its clients’ identities, information about customers is more integral to success than ever before. Entire businesses and industries have grown with no economic model other than collecting vast hordes of data, and reselling it for use by others.

That data, however, has begun to be seen by business leaders as a panacea. Many have been trained to believe that relentless measurement is the core discipline in managing a company—a focus that has stripped away the complexity involved in managing customer relationships and converted them into equations. X outreaches multiplied by Y conversions equals Z opportunities, which yields a number of customers. And yet, people are much more than variables: as one Atlantic writer pointed out, just because a customer’s frustration with a brand can’t be measured doesn’t mean that it isn’t relevant.

It’s misguided to think that data is a catch-all solution for business leaders. If it were possible to create the perfect equation for business success, then there would be no failed companies! It would be a simple matter of calculating the correct number of outreaches, and sitting back while the money rolled in. Successful organizations see data as one input, but recognize it is the unique blend of that science with the art of relationships that moves them forward.

Take, for example, the diaper industry in the 1980s: at the time, each company’s diapers were indistinguishable from one another’s, making it hard to stand out in the market. The dominant player in the industry was Proctor and Gamble, which had achieved 75 percent market share at the time thanks to their Pampers brand. P&G had developed a superior product which offered better fit and greater comfort, both of which came at a greater cost overall.

P&G’s data analysts were clear: people weren’t willing to pay more for their diapers than they absolutely had to. The product launch went forward, but in deference to the data, P&G created a new brand, Luvs, anticipating that it would not be successful. And yet, Luvs quickly came to dominate, as parents preferred the better shape and fit, even with a price premium. P&G was still the undisputed champion of the diaper world, but with two competing products they created an opportunity for competitor Kimberly Clark to challenge P&G.

Like P&G, Kimberly Clark relied on data, and had some of the best researchers in the industry. It  could analyze customer selection and baby physiology, but it wouldn’t be until the company’s executives started seeing that data as one piece of a larger puzzle that they were really able to differentiate themselves from P&G. Overreliance on data and experimentation had created tunnel vision within Kimberly Clark that depicted parents as solely focused on getting rid of their child’s waste. This led to marketing, customer surveys, and other efforts being specifically focused on besting P&G in this area. Kimberly Clark saw its relationship to customers as an equation: if it just followed the data, emphasized key features, and increased inputs, it could dethrone the diaper king. The success of Luvs opened their eyes: The customer data was deceptive. The real question wasn’t scientific, it was emotional: What does a diaper mean to a parent?

It came down to caring for and nurturing their children; diapers aren’t just a solution, but an aid parents could use as a means of keeping their children comfortable and address fears that their children would be delayed in their development. This new understanding came about as a result not of abandoning the data altogether, but instead merging data with a keen understanding of people’s real emotional drivers, fears and goals. As a result, Kimberly Clark launched Huggies Pull-Ups, which became a dominating force in the diaper market.

Customer data was incredibly important to the success of both Luvs and Huggies. But the data were also deceptive when used in isolation, and led both P&G (in forming a brand new brand and diluting its share) and Kimberly-Clark (by initially emphasizing a product benefit) in erroneous directions. Only when the data were seen as a piece of the overall customer equation was success created.

If the amount of data available 40 years ago could be deceiving, it’s easy to see how today’s volume can obfuscate even the most sophisticated organizations. Walmart is looking at deploying robots in its stores to help with restocking efforts, which would enable a redeployment of personnel and resources away from scanning shelves towards more efficient uses of time and money. It’s typical data-driven decision making: the numbers on a spreadsheet clearly indicate this decision will save money and make the organization more profitable.

Unfortunately, the data can’t reflect how Walmart customers feel about seeing robots replacing their fellow human beings in the store. Turns out, customers don’t particularly care for the robots, and the move makes workers feel awful: replaceable by machines to increase corporate profits rather than a valued part of the Walmart brand. Walmart’s core brand identity is predicated on convenience and price point, but it’s intuitive that unhappy and disengaged employees will lead to customers facing increased frustration. Ignoring the human element, again, means the data might not be as accurate as many believed.

That’s why companies like Trader Joe’s are ignoring purely data-driven decision making, embracing inefficiency—and growing like mad.

Trader Joe’s violates a lot of what might be considered retail best practices; it keeps its stores small, its selection narrow, its advertising limited, and completely forgoes coupons or loyalty programs. Forget using data, the lack of a loyalty program means the company often isn’t even bothering to collect it—a forceful rejection of other retail chains who make this a center point of their data strategy. Trader Joe’s is willing to surrender a certain competitive edge to more data-driven competitors, such as Whole Foods, in exchange for securing another: employees focused not on specific metrics, but instead on customer satisfaction.

Trader Joe’s takes a different approach in how the company employs its staff. The company hires extroverts that enjoy talking to customers, and pays above-average wages and benefits. Trader Joe’s replaces the industry-standard practice of overnight stocking with doing it during business hours, which brings these employees into greater contact with customers in greater numbers than they otherwise would have. Looking purely at the data, executives in similar organizations have made decisions that mirror those of Walmart: depressing wages, reductions in staffing, or other changes that are antithetical to Trader Joe’s, such as poor quality or expanded products. In rejecting all of those, the company continues to grow, and be immensely profitable, due in no small part to the unquantifiable love its employees have for the company.

Data is a component of what propels any organization forward. It helps discover problems, and support potential solutions. But the cold comfort of numbers, and their perceived certainty, makes it all too easy to become addicted to data, causing organizations to lose sight of what can’t be quantified. It’s only when those human elements, the art to data’s science—employee satisfaction, customer evangelism, experience, and more—are valued equally can brands develop an equation for sustainable growth.

Mike Dea is an associate at Woden. Want to stay connected? Add him on LinkedIn, read our extensive guide on how to craft your organization’s narrative, or send us an email at connect@wodenworks.com to discuss whatever your storytelling needs may be.