Online merchants are cashing in on Big Data, using analytics to generate incredibly valuable consumer insights. According to The Financial Times, retail giant Walmart parlayed Big Data insights into a 10 percent to 15 percent increase in completed online sales, netting an astonishing $1 billion in incremental revenue.
Until recently, brick-and-mortar operations found it difficult to replicate that success, partly because there were fewer opportunities to capture data during offline shopping. But thanks to the ubiquity of always-on, always-connected mobile devices, it’s now more possible to collect and apply Big Data in an offline retail environment.
In the retail sector, Big Data typically includes all the consumer data that companies can gather and process. Big Data is drawn from a near-limitless variety of sources, including online browsing and buying patterns, PC-generated information about online shopper locations, cash register receipts, data that companies purchase from third-party analysts, and other sources. Finding the right mix of data is akin to adjusting a recipe for individual tastes: Depending on a company’s target audience and unique sales and marketing strategy, one blend of sources may work better than another. For example, an online retailer looking to increase revenue from existing customers would benefit from loyalty program membership data to get a clearer picture of customer preferences. A campaign focused on new customer acquisition might rely on data generated by third parties.
Now there is a new data source: data collected in stores from mobile phones, which I refer to as “clean data” because it is stripped of identifying consumer information. It is generated by proximity marketing technologies that collect Bluetooth and Wi-Fi signals from consumers’ mobile devices to anonymously gather shopper information like what areas of a store customers visit, how long they stay, whether they accept or decline digital offers – all without compromising consumers’ privacy.
Because clean data collects only the device signal without gaining access to identifying consumer information (such as phone numbers, contacts, etc.), it protects consumer privacy. And because it gives consumers the opportunity to accept or decline offers like electronic coupons, any direct commercial contact occurs only after obtaining consumer consent. The use of familiar technologies, such as Bluetooth and Wi-Fi, which are known by most consumers to be mature technologies with a low risk of hacking, and the active affirmation requirement can inspire greater consumer confidence than passive data-collection techniques that gather identifying consumer data without active shopper consent.
Clean data collected in-store has the potential to upend established notions about customer behavior within brick-and-mortar establishments in the same way that advanced analytics of web site visitor behavior changed the way e-commerce strategies are developed and executed. It provides insights such as which offers shoppers find most attractive, what areas of the store are most productive, how shoppers interact with distinct media elements and how much time consumers spend in the store. It gives brick-and-mortar retailers information that helps them refine offers and accurately gauge and respond to customer preferences and behavior in real time, enabling stores to cultivate and manage customer relationships in the same way that companies like eBay and Amazon influence customer relationships and buying behavior.
So how much are insights like these worth? Working with proximity marketing specialist iSIGN Media, Dr. Jeff Tanner of Baylor University’s Hankamer School of Business estimates that clean data is worth approximately $0.20 per unit of insight. However, when combined with other data categories, such as cash register information, the value can be increased exponentially by giving merchants a 360-degree view of customer behavior – online, on mobile devices and in stores.
Big Data-generated business intelligence has already transformed the way successful companies develop and execute online advertising and e-commerce sales strategies. And as the volume of data continues to increase exponentially across both the online and offline spheres, brick-and-mortar merchants will begin to generate profitable insights from Big Data.
And because clean data delivers insights without compromising consumer privacy, it helps merchants avoid controversies in an environment marked by heightened privacy concerns. A valuable commodity in its own right, clean data can be combined with other data streams to deliver even more detailed insights – and opportunities for merchants to generate additional ROI.
Alex Romanov is President, CEO and a director of iSIGN Media.