I recently wrote this comment in a RetailWire discussion on the use of Big Data in brick-and-mortar retailers (I warned you that this material would start making its way into my blog):
There’s a big difference between data and knowledge. Data are necessary to take store management out of the realm of guesswork and into that of true, fact-based decision making. But data points on their own are highly difficult to work with, especially when we’re talking about the complex, nuanced environments we’re seeking to optimize.
Our customers at RetailNext have achieved great success by using a solution that takes the huge quantity of data available and converts them into views and reports that yield actionable insight. Our solution measures more than 9000 individual data points on the average store visit. Nobody wants to examine 9000 data points per visit. Instead they want to see correlations, comparisons, and models that help them understand the true behavior in the store. They want to see heat maps of traffic and how those maps change over time. They want to see the cyclicality of the day, week, and year and how that compares to staff schedule. They want to see how all of these things correspond to actual sales at the register. And they want to be instantly alerted to certain conditions like when a certain item is near empty on the shelf or too many people are standing in line. That’s how Big Data can be useful, when it’s translated into actionable insights that make shopping better.