Retailers need to make data-driven decisions—and they need to make them quickly. Here are some tips for making sure your insights are truly actionable.
Agility and data-driven decision-making in retail are buzzy topics. According to Microsoft’s 2019 Retail C-Suite Viewpoint Survey, leadership continues to demand data-driven strategies for transformation, with 48 percent working towards short-term goals and 41 percent dealing with internal resistance to change.
But for many teams, executing on data still feels impossible. How on earth do you make data-driven decisions quickly when you’re at the helm of a cruise-ship-sized retail brand?
More importantly, how do you do it quickly? And is acting fast really the best approach?
Operational agility looks different for every retail organization, but all successful retailers have one fundamental thing in common: An unwavering focus on the customer experience. Here’s a short list of reasons to take fast action when you see data blips and anomalies—and advice on how to notice them in the first place.
Customers are fickle. Are you able to appreciate them in time?
Ultimately, data is valuable only insofar as it allows you to know what your customers want and consistently meet those expectations. But you can’t sleep on those expectations; today more than ever, customers demand instant gratification and appreciation.
Infoworks, an agile data engineering platform, offers a real-world example of what this looks like in a retail environment:
“One of the companies we work with started as an “old-fashioned” brick and mortar retailer. But they were smart and realized years ago that they were facing an existential threat from the emergence of online retailers like Amazon. They invested early in online initiatives, which now account for over $1B per year in revenue. But, in 2018 they realized that in order to keep up competitively, they needed to more aggressively expand and improve the value of their customer loyalty program. And while they could already look at what a customer purchased in their physical stores, analyze it and make a complementary offer, that offer wouldn’t arrive in the customer’s email until the next day.”
To be more agile, the retailer changed two key elements of its strategy:
They enabled real-time data aggregation. As Infoworks notes, “This meant tapping decades of customer purchase history information, store inventory data, and click-stream data from the retailer’s website—all during the few seconds just after the customer has completed a purchase.”
They switched to mobile/SMS marketing to send relevant offers to customers sooner. Rather than sending an email the next day, they can now text a customer an offer before they even leave the store.
Collect good data—and make it better
Most retailers already know that data is key to surviving in today’s ultra-competitive, constantly evolving arena. In that regard, they’re making big strides to collect the right data with new and improved tools.
But simply collecting data isn’t enough. You also need to make it your own. This means distributing it to the teams that need it in a way they can understand. A key component of this is enabling teams to run useful reports with usable analytics platforms.
Making the most of your data also involves improving your data quality over time. Eliminating data silos to enrich data for deeper insights is one key way to make sure your data is always as meaningful as possible, but benchmarks are also crucial. Are you able to compare today’s performance to last week, last month and last year? Can you identify and implement benchmarks that give your current data context?
So what’s holding you back?
One of the biggest barriers to agile, data-driven decision-making is that retailers just don’t have the tools. Many are coping with older infrastructure or highly customized app ecosystems that create a sizeable technical debt.
A common symptom of this problem is data silos: Is the data you need owned by a different team or stakeholder? Does the data live in one app or system, and do you have a hard time pushing it into other platforms?
If the answer to any of those questions is “yes,” it might be time to conduct a thorough audit of your data toolkit. Evaluate the tools you’re using to collect, store, manage and measure data. Chances are there are plenty of easy ways to implement data best practices and increase agility—without overhauling your entire tech stack.