In this special guest feature, Sarah Kampman, VP of Product at Square Root, discusses why retail is positioned to reap the biggest benefits of data analytics today. Sarah shares why machine learning and AI may be the secret weapon to solving for the the challenge of operating hundreds — even thousands — of disparate store locations, and how the approaches can be used to drive store performance, increase alignment and impact decision making at every level of a retail org. As the VP of Product at Square Root, Sarah finds solutions for customers that they didn’t even realize they needed. With 15 years in product management and more than 20 years in technology overall, Sarah specializes in creating long-term focus groups with valued clients, helping them use technology to meet their business needs. She understands that building relationships through empathy leads to the most dynamic ideas and strategies. Driven by a passion for behavioral economics and a desire to study how people make decisions, Sarah received a BA in Cognitive Science from University of California at Berkeley.
Data science is a big buzzword in business today. Beyond the hype, organizations are using advanced analytics to do everything from understanding their customers to improving forecasting, driving better, faster results. While the impact of these approaches is being felt across nearly every industry, retail stands to reap the biggest benefits. With more big box retailers announcing layoffs, store closures, and bankruptcy, data science may just be the secret weapon for success.
Well-positioned to win
Retail organizations are among the most complex, with thousands of employees across multi-layered teams, and hundreds or thousands of disparate locations. Competition is increasing every day, with new market entrants ranging from brick and mortar stores to home delivery services. Brands must also deal with the complexities of omni-channel integration — seamlessly integrating a constantly growing number of shopping channels — from online to in-store to mobile. They’re expected to deliver a consistent brand experience, while also providing a personalized experience across all channels.
Retail is an innately people-oriented business which makes it ripe for data science impact. Despite the massive amounts of data available, many of today’s decisions are driven by human observation and opinion. This leaves room for bias and error, and wastes time in human-directed data analysis that could be better spent taking action on the insights.
Success in retail operations relies on equipping teams with critical information, empowering them to take swift action. However, the industry has fallen behind in providing the necessary tools and technology. Retail teams are often stuck using outdated tools like manual spreadsheets, legacy technology — even pen and paper — to analyze data across the business. In a recent survey, nearly half of all Store Managers and more than half of all District Managers reported that they rely on aging technology to perform major functions of their role. Topping Store Managers’ wish list was better software, with 25% reporting it would positively impact store performance.
Putting data science into action
Data science has the potential to unlock insights to win and retain customers, drive business efficiencies, and ultimately improve performance. It can also help retailers uncover trends, but more importantly, data science can identify the KPIs’ drivers to make smarter, faster decisions.
Consider a retailer’s cross-promotional marketing efforts. Despite the same external promotions, sales for a particular brand of jeans are up at one store, while at another store, sales of the same jeans are flat. The store manager may assume it’s simply consumer preference. But advanced analytics can reveal that the increase in sales was related to an in-store cross-promotion with a sneaker brand. That actionable insight can now be shared with other stores to improve sales.
Customer experience is another area where data science can support data-driven decisions. Decisions today are heavily influenced by human bias, driven by what retail leaders believe. But managers are often only half right when it comes to understanding in-store problems and customer behavior. Data science can help combat that bias, arming managers with data insights and best practices to make tailored improvements to the customer experience.
Lastly, an often overlooked area for data science and one of the biggest opportunities to influence performance is employee satisfaction. In an industry where people are at the center of success, retailers must get employee satisfaction right. When reviewing eNPS, corporate teams can leverage natural language processing and correlational analysis to uncover what’s driving low satisfaction and help their stores solve for those challenges, improving satisfaction and retention.
Although the finance and logistics arms of retail have already embraced data science, applying advanced analytics to store operations is an as-yet untapped area of opportunity. Data science can help retail operations leaders make smarter, faster decisions. Those who get it right will find themselves quickly pulling ahead of the competition, with the insights needed to win customer loyalty, drive business efficiencies, and ultimately improve performance.
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