Why Big Data Matters for Retail
Retailers today have access to more data than ever. Scheduling software tracks time and attendance, inventory software keeps track of goods and materials, learning-management software monitors training completion and internal certification, and human resource management systems track the front-line workforce. Despite the terabytes of data that are being generated on a daily basis, relatively few retailers have figured out how to use that information effectively to drive business decisions that will impact the P&L. An enormous gap exists between the potential value of this data and how they are actually being used. There lies an opportunity to stay competitive in a tough industry.
Target is often cited as one of the first retailers to take advantage of big data. To understand how to market new products to their customers better, analysts at the retailer studied years and years of its customers’ purchase data. The company’s people were looking for patterns that would enable them to predict the future. What they found, as one example, was a way to spot a pregnancy in the first trimester, based on changes in a woman’s buying behavior. One clue? A switch to unscented lotion.
But big data can be used for more than just anticipating what a customer will buy. Brick-and-mortar stores sit in an interesting nexus. If a consumer simply wanted to buy merchandise, he or she could easily visit a website and get materials shipped. Consumers who visit a brick-and-mortar location are looking for a personal connection and real, live customer service representatives.
How do you make sure that the best workers are in those customer-facing roles and are consistently adding value to the business? The key is using big data in management. Smart retailers are now using data from a variety of different sources in order to make better decisions about how to manage their employees throughout all stages of the employee lifecycle: Which job applicants are best suited to interact with customers? What sort of overtime policy yields the best employee retention? What types of supervisors are best suited to manage their front-line employees? Data can provide more informed answers to all of these questions and can help large retailers get the most out of their retail workers.
For example, one of Evolv’s findings, with the help of researchers at such academic institutions as Wharton School of Business and Northwestern, is that job applicants often try to game pre-hire assessments by exhibiting a tendency to exaggerate their own knowledge, skills, and abilities. No surprise. But when managers develop a more fine-tuned approach to measure applicant honesty and integrity by asking people to self-assess their computer skills and then test them on those skills, applicants whose actual skills map to their predicted skills perform on the job better. They stay 54% longer, they exhibit 6.8% less absenteeism, and are 1.7% more productive on the job. Retailers focused on improving schedule adherence and reducing shrinkage are using these insights to hire and develop a better workforce.
Remarkably, most retailers already have in their possession the data that can be used to engage in this sort of analysis. The problem isn’t generating this data, but gaining the ability to appropriately to make sense of the data. The good news is that there are more and more tools becoming available that are democratizing analytics. These “PhD-in-a-box” solutions make it possible to do the same types of analyses without manually building a data warehouse and hiring a team of engineers.
Retailers that want to remain relevant in today’s increasingly competitive talent marketplace can no longer rely on traditional approaches to management, especially in brick-and-mortar retail. The volume of data that can be used to improve workforce operations is growing exponentially by the day. Retailers have a huge opportunity to transform the ways that they manage the workforce if they utilize the valuable data assets that are already in their possession.
How have you used big data recently?
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