How To Stop Big Data from Creating Big Distance Between You and Your Customers

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Hype-Cycle_2015_1

Source: Datanami

In August of this year, a very curious thing happened: The industry analyst firm Gartner, perhaps the closest thing to an official arbiter of buzzed-about technology, dropped “big data” from their closely watched “hype cycle.” Having previously crested the “peak of inflated expectations” and begun its descent into the “trough of disappointment,” “big data” simply disappeared, before it could seek out redemption in the “slope of enlightenment” and “plateau of productivity.”

In a blog post accompanying this decision, Gartner suggested that “big data” was dropped from the hype cycle because it has now been normalized into the very fabric of how we do business. But if the technology of “big data” has become ubiquitous, what does that tell us about the rhetoric of “big data”? If we’re now on the other side of a transition to bigger, faster and more comprehensive data tools, then what has become of the promises that “big data” would transform the very fiber of our world? The promises that big data would allow us to predict the future with absolute certainty? The promise that big data would allow us to understand our customers better than they understand themselves?

In the cold light of a world optimized, but not upended, by “big data,” these promises may seem like little more than overblown marketing rhetoric (or, as Maciej Ceglowski beautifully put it, “investor storytime.”) And while the tools and technologies of “big data” have had a real positive effect on numerous businesses, the false promises of “big data” have had just as real of a negative impact on others. By promising that fixed models could predict the future in a world that changes faster by the minute, “big data” left some companies less capable of adapting to real-world change. And by promising a future in which dashboards and quantitative analysis could be used as a stand-in for true human complexity, “big data” left some companies further from a true understanding of their customers.

Case Study: Tesco

Source: Forbes

One need look no further than British supermarket chain Tesco, once the standard-bearer of “big data” case studies, to see how these two myths could lead a company astray. Tesco famously used “big data” to determine everything from the temperature of its refrigerators, to the amount of inventory to purchase each season, to an exact personalized discount for each individual customer. And while these strategies won glowing press coverage and temporary gains for Tesco, it left them with a huge blind spot as the quality of consumer attitudes shifted. In piece starkly titled “Tesco’s Downfall Is a Warning to Data-Driven Retailers,” the Harvard Business Review outlines how, as Tesco relentlessly tweaked and improved their models for highly adaptive and personalized discounts, their customers lost interest in the very idea of personalized discounts. As the Telegraph observed—from actually talking with Tesco’s customers, rather than quantifying and analyzing them—“ one of the reasons that consumers are turning to [German supermarket chains] Aldi and Lidl is that they feel they are simple and free of gimmicks.”

Case Study: Timberland

timberland

Source: Bloomberg

Now compare Tesco’s approach to that taken by Timberland, a company that used “customer data” to reinvent its business. Using much more traditional methods (primarily surveys and interviews), Timberland learned that many of their customers were waiting until Timberland products went on sale to make a purchase. So they took a bold and counterintuitive action, “cutting back drastically on promotional pricing.” As predicted, revenue initially fell as customers waited for the next big sale. But when that sale never came, slowly but surely, more customers began purchasing at full price. Within a year, the percentage of full-price sales made on Timberland’s website climbed from 36 to 88. Rather than using “big data” to turn their customers into quantifiable bits, they understood that real people can have complex, dynamic, and even contradictory needs. And, more importantly, they actually listened to their customers even when it meant going against some of their longstanding beliefs and practices.

What Can Your Company Do?

So, what can your business do to avoid the traps and pitfalls of “big data” rhetoric and create a data strategy that actually helps you meet customer need?

  1. Get Out of the Building and Talk to People

If you want to understand your customers, talk to them. Quantitative data alone can’t provide you with a holistic view of people’s behaviors, needs, and worldviews—especially as those behaviors, needs, and worldviews change and evolve.

  1. Have a Clear Business Purpose

Many companies that jumped on the “big data” bandwagon without a clear purpose are finding that the investments they made are now little more than sunk cost. In fact, the average ROI on “big data” tools and technology is about 55 cents on the dollar. This isn’t because these tools can’t deliver value, but rather because they can only deliver value when being used towards a clear and well-understood purpose.

  1. Make Data Literacy a Core Competency

The best way to debunk “big data” mythology is to understand how “big data” tools and technologies actually work and what they can actually do. Make sure everybody on your team is data literate enough to understand both the power and the limitations of the systems they’re using to make decisions, and you’ll be far less susceptible to overblown promises that yield disappointing—or even disastrous—results.

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