The rising popularity of mobile devices, specifically smartphones and tablets, is driving explosive growth of network connections—Cisco’s 2012 Visual Network Index Forecast predicts that nearly 19 billion network connections will be operational by 2016. This proliferation is resulting in the availability of an unparalleled amount of gathered data, referred to as “Big Data,” a phenomenon sometimes described as the “oil of the 21st century”.

In addition to gathered data skyrocketing, mobile data is rapidly accumulating as people continue to rely on mobile devices. In December 2012 Mary Meeker, Managing Director at Kleiner Perkins and longtime market analyst, shared her Internet Trends Update estimating that there are 1.1 billion smartphone users globally, and that number is expected to grow over 42% annually (Source: Mary Meeker 2012 KPCB Internet Trends Year-End Update).  For retailers, this growth is especially intriguing. According to the National Retail Federation’s Mobile Blueprint, consumers worldwide are expected to use mobile devices to make purchases worth up to $120 billion by 2015. The evolving mobile market makes it increasingly important that retailers understand and cater to mobile customers.

With every touch, click and purchase, consumers generate revealing data—data that can be collected, stored and analyzed to retailers’ advantage. For example, during the day, users often check into various websites and apps with their smartphone from home or the local coffee shop on the way to work. This activity can occur 24 hours a day, from an infinite number of locations. By tracking these actions, retailers could potentially base their marketing strategies on quantitative, behavioral data rather than on subjective guesswork or abstract approaches.

However, many retailers aren’t prepared to manage this explosion of mobile-generated data. Marketing departments usually don’t have the tools in place to understand mobile customers’ actions—what they do, when and how often—despite the amassing data now available to answer this question. And, while retailers may now have access to this wide reserve of information, big data is just a storehouse of dusty bits without real-time analytics and data scientists. If companies can’t take that data, analyze it and put it to operational use, gleaning any real value from it is impossible.

The disconnect between mobile data and related action from business is evident in Meeker’s research, which points out that we spend 10 percent of our media time on mobile devices, but the advertising industry only spends one percent of its budget on the mobile channel. As retail continues to draw more mobile users, and data about these users continues to pile up, the question becomes: How can retailers harness mobile-generated data to their advantage?

Find the right tools. Retailers should make sure their tools address the growing mobile marketplace by designing or incorporating mobile tools—including Android, iOS and HTML 5 apps—that enable rich data collection. If you don’t log each and every click or event, you’re not painting a full picture of your customers. It’s essential to implement a comprehensive way to instrument your apps (like with a software development kit), then log data, pull it into a big-data structure and begin to analyze and build recommendations from it.

Think predictive. Retailers should develop predictive models to identify and monetize mobile customers. Businesses now have the capability to use predictive analytics to identify what users are likely to do in the future—knowledge that can put retailers one step ahead.

Leverage promotions. Once proper predictive models are in place, retailers also should develop effective incentives to retain customers and reduce churn. These promotions can draw on the insight gleaned from predictive analytics to send out offers that will resonate with customers. For example, predictive analytics can help a business understand that a customer who purchased a jacket two years ago is likely to be in the market for a replacement this winter. This retailer could then convert this insight into a purchase by offering this customer a coupon for a similar style coat.

Close the loop. The right set of predictive models can help retailers retain customers, in part by allowing them to deliver relevant ads, offers and promotions. The only way to do that in a balanced fashion is with what is known as closed loop optimization, meaning that as the user clicks on the mobile app, the data is logged and a recommendation is produced.

Focus on real-time. With mobile shopping emerging as the next evolution in commerce, retailers must focus on collecting and analyzing a valuable new set of data. Technologies like Hadoop are critical, but the real-time component is what makes big data actionable. Real-time analytics allow you to see what your users are doing at this instant and act on this knowledge quickly, rather than solely looking at historical trends.

Remember the pink unicorns. Customer information and predictive analytics are only two-thirds of the data science experience. The third component: human experts, known as the ‘pink unicorns’ of the industry. The ‘pink unicorns’ are data and predictive-analytic scientists. They’re the ones who must constantly adopt new technologies and tune analytics for each industry, including retail.

As the abundance of data, especially on mobile devices, continues to explode, big data will continually become more of a marketing than a technical issue. Retailers would be well advised to leverage the above tips as best practices to improve their data strategy, reach mobile users and, ultimately, maximize industry gains from the mobile-data revolution.

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About Brian Lent

Brian Lent has more than 25 years of technology, entrepreneurial and product leadership experience. A sought-after authority on Big Data and predictive analytics, Brian co-founded MIDAS (Mining Data at Stanford), the lab that incubated the Google search engine, before going on to develop technology later acquired by companies like Amazon and First Data Corporation. At Medio, Brian oversees the development of Medio’s growing predictive analytics technology, which has helped Fortune 500 and emerging growth companies gain deep understandings of their customers to improve engagement and monetization of their Web and mobile applications.