The data streaming through new digital customer-facing touch points has helped omni-channel retailers understand customer demand and behaviors, but more importantly, this information is the secret sauce needed to transition to a customer-centric merchandising strategy. By merging next-generation analytics and integration strategies that leverage shopper-specific big data, retailers are primed not only to better understand their shoppers’ purchase journeys, but also use this demand to predict and plan enterprise-wide customer-centric merchandise assortments.
In this Q&A with Jeff Buck, Vice President of Analytics for Aptos, we explore the emerging role of analytics as a key driver of customer-centric assortments.
Q: As omni-channel creates more data sources for retailers to use to enrich forecasting and planning, how is this changing the way retailers are executing their merchandising operations?
JEFF BUCK: Retailers are increasingly looking to break down the silos within their organizations in order to better understand and service the omni-channel shopper. We find many examples of
this at the point of purchase, but changes to back office processes and culture are less obvious from an external perspective. The major “unlock” or opportunity in removing “silos” is in the cross-pollination of data from marketing to planning and merchandising. A detailed understanding of consumers’ preferences and behavior — made available to both merchants and planners — is
revolutionizing the merchandising world. The availability of consumer insights (formerly locked away in marketing) has changed traditional planning practices and paradigms.
It’s no longer about products and stores, but about customers and channels. As more and more marketing data makes its way into merchandising, we are seeing a growing number of retailers turning their planning hierarchies on their side and planning by customer segment, rather than product category. Those that embrace the change have seen assortments that are much more closely aligned to customer preferences, and their organizational cultures are shifting to be much more closely attuned to the customer.
“As more and more marketing data makes its way into merchandising, we are seeing a growing number of retailers turning their planning hierarchies on their side and planning by customer segment, rather than product category.”
Q: What are the biggest challenges retailers are dealing with when it comes to maximizing advanced analytics in their management of merchandising operations?
BUCK: I think that the biggest overall challenge is the task of collecting, managing and acting upon the sheer volume of granular data (from an unprecedented diversity of sources) that is available today. Retailers need help managing the changes that come about when advanced analytics are introduced into the business. This means process and cultural change management as well as the provision of tools that are designed to help them understand their business and to maximize their opportunities in a complex and evolving environment.
Q: How can retailers use advanced analytics to achieve tighter forecasts, more accurate merchandise plans, fewer overages and underages, and optimize profits across channels?
BUCK: Advanced analytics offer insights and opportunities that would be missed using conventional reporting methods or a manual/Excel-based approach. Given the amount of information involved and the time/tool constraints on the business user, people typically only have time to look at a subset of their data (best performing classes or styles, worst performing, etc.) Without tools that provide easily discernable insights and that quickly highlight opportunities, most of the data is not reviewed in detail. Hence, the potential to achieve more accurate plans and optimize metrics is not attained. Analytics should also provide new data points such as “stockout over time” or “lost sales” that can guide the merchant team to not only make more informed decisions but to prioritize each opportunity in order to focus on those that will generate the greatest profit or sales.
“Analytics should also provide new data points such as “stockout over time” or “lost sales” that can guide the merchant team to not only make more informed decisions but to prioritize each opportunity in order to focus on those that will generate the greatest profit or sales.”
Q: What benefits does the cloud-based model provide to retailers seeking analytics-driven merchandising capabilities that will achieve ROI?
BUCK: The cloud-based model offers retailers the opportunity to adopt tools that might otherwise be out of their reach, due to constraints such as initial investment and ongoing maintenance of infrastructure. Budget dollars that were previously spent keeping the IT infrastructure up and running can now be re-allocated to provide business benefit, and to deliver benefit in a much timelier manner (the time investment in infrastructure is typically less than that of an on-premise model). Cloud models also allow lower-volume retailers to take advantage of more robust BI and analytic solutions that previously would be out of reach.
This Q&A was excerpted from “Analytics Powered Merchandising Makes Customer-Centric Retailing Possible,” a June 2015 RIS News retail IQ Report