Developing Retail Talent in the Age of Big Data | RetailNext

Comprehensive In-Store Analytics


Developing Retail Talent in the Age of Big Data

Bridget Johns
Bridget Johns
Head of Marketing and Customer Experience

Beyond operations, merchandising, and marketing, in-store analytics can take retail professionals from good to great.

When I was graduating from college, I remember retailers like Ann Taylor and the Gap would recruit aggressively for students to enroll in their management training programs. Later, when I ran retail stores myself, I often looked for the same kind of experience in the candidates I was hiring. I knew these structured programs gave young retailers a solid foundation in management skills, which would always lead to better decisions on the job. Unfortunately, over time, many of these programs were eliminated or downsized, and somehow retail became a somewhat accidental career for many. I am personally very grateful I chose retail as a career and in doing so I have had opportunities beyond my wildest expectations. That being said, I wonder how younger retail professionals are getting their experience and honing those important retail instincts.

Anyone who has spent even part of their career in retail can attest there are retail legends who always make the right decisions. Whether it be experience, luck, or instinct, these professionals seem to know exactly what the customer wants, at what price, and when. Retail legends such as Mickey Drexler, Ralph Lauren, and Terry Lundgrun, along with countless lesser-known retailers have this gift. But while these legends are brilliant in their field and some of their success was reflective of natural intelligence, they weren’t necessarily born with their intuition. Instead, as Malcolm Gladwell explains so convincingly in his bestselling book Outliers, extraordinary success is a result of natural talent and a lot of practice. I would even go a step further to argue that these things lead to incredible instincts.

The National Retail Federation recently launched a great initiative called This is Retail, promoting the industry as an attractive field for young graduates to pursue. Similarly, some retailers like Kohl’s and Macy’s are actively recruiting on college campuses. But as encouraging as this is, I wonder to what extent these initiatives are grooming the retail intuition and recognizing the rapidly shifting landscape. After all, the retail industry has changed dramatically over the years, thanks to the total disruption of long-standing paradigms by online, social, and mobile commerce. Amazon, for one, has forced retailers industry-wide to give customers a compelling reason to shop in their stores, and everyone has an omnichannel strategy. At the center of this evolution is the idea that data can help retailers build a better mousetrap.

While implementing this idea as a strategy has clearly worked for ecommerce, the dynamics of brick-and-mortar retail are far more complex. Inside a retail space, it’s not about a customer’s clickstreams, but rather her physical path, her movements and behavior, the activity on her phone, the people around her, the ambient sights and sounds, and a nearly limitless number of other variables. In fact, there’s a plethora of data about that experience—and not only about the shopper herself, but also about things like product placement, pricing, and inventory. The good and bad news is that there are more of these data points than a retailer can possibly know what to do with. One of the questions I’m most commonly asked in my role as Head of Customer Engagement at RetailNext is, “What do we do with all that data?”

Well, there are some obvious answers to that. Using that data, you can:

But my point isn’t to discuss how the operational and marketing needs of your store can be met using analytics data (on that subject, I invite you to visit other areas of the RetailNext website for previous blog posts and webinars). My point here is that through the use on in-store analytic data, we can take retail professionals who are good and make them great.

Imagine you have a young, fantastic store manager named Megan. She doesn’t have much training but her teams love her and she follows the corporate handbook meticulously. Her store isn’t the best performer, but it’s always towards the top. Megan believes in the brand and is a great ambassador, so in turn, senior executives of your company think she’s phenomenal and are always asking about her. As her manager, you think to yourself, “She could be better, but how do I get her there? She has a lot of great qualities but she is missing that certain intuition about what she can do to drive her business.”

You decide to start providing her with two data sets. The first shows staffing across hours of the day, aligned with conversion, and provides some recommendations for how to better align the store to meet the increased traffic in the middle of the day.

The second data set shows her performance in comparison to other stores in your chain, not in a static table from top performers to bottom, but in a quadrant that aligns performance through the understanding of conversion and average transaction.

Blog post performance quad graphic

Megan is able to see her performance in a more dynamic way, and through some coaching tools understands what she needs to do to move to the “best” quadrant. Over time, these actions become intuitive and Megan starts to proactively look for data when store performance is lagging.

As Megan’s store’s performance improves and she is promoted to manage her own district, she immediately knows what to look for—her intuition, prompted by the data, tells her that some stores have a problem with conversion, some have a staffing alignment issue and some just aren’t attracting the right traffic to begin with. Issues that would have taken weeks or months of understanding previously can now be understood and addressed in a matter of days – assuming the data is there. 

It’s really that simple. Yet amazingly, many retail managers don’t take advantage of the tools that generate valuable data about their operations. The direct consequence is obviously the loss of sales. But of equal importance is the failure to train employees to excel in the ever-changing retail world, a world that demands quick, data driven decision making skills. After all, the ability to make these decisions quickly becomes ingrained and will ultimately become part of retail intuition. And the retailers who use data proactively to drive business and employee growth will be the ones who win.