The word “optimize” was littered across the expo floor at NRF 2019.
From shelf space to e-commerce basket sizes to your in-store workforce – every process across the retail value chain seems to be begging for optimization. “Frictionless,” “efficient,” and “seamless” were the all-encompassing descriptors used to entice interest and engagement among attendees perusing the aisles for technology solutions, ideas, or (let’s be real) free giveaway swag.
The need for optimization is unequivocal as retail organizations are tasked with executing costly decisions on a daily basis at a tremendous scale. Yet how to approach this problem is less than certain when there are numerous opportunities to improve decisions at each stage of the retail process. Whether your current struggle hinges on production, inventory, store operations, or last mile fulfillment — whatever the challenge may be — the technology available today will enable retailers to glean insights into the mountainous amount of data they have.
Based on our observations at the big show in NYC, it’s apparent retailers must prioritize investments in tools like artificial intelligence (AI) and advanced analytics to make the optimal decisions to meet the ever-evolving needs of their customers.
Optimize to Meet Customer Needs
What if we could know – really know—what our customers wanted? Before they even know they want it? Marvin Ellison, CEO at Lowe's, stated at an NRF 2019 keynote session last week the #1 retail fundamental is to ‘be in stock.’
At retail’s core, being stocked with the right product is everything. The industry’s success, in its simplest form, is reliant on this fundamental task. While simple in theory, the reality is much messier and complex. But what if you had the tools necessary to clearly see what drives customer preference? How one product influences demand in another? How a customer walking into your store is likely to choose between products?
At NRF, many sessions spoke to this challenge. As evident throughout the show, advanced analytics tools are making headway into the merchant’s strategies, offering teams the ability to sift through a multitude of data points to detect future patterns and behaviors of a customer, and their propensity to buy a particular product. This ability to detect likely purchase behavior before a customer even walks into your store is the underpinning strategy retailers will pursue to enable optimization across the retail value chain.
Optimization in Action
The allure and need for AI is clear – yet, as with any new technology, the execution piece is still working its way into the fold.
Optimization use cases for AI range from product development to pricing to fulfillment, and many of these stories were broken down at the show to make a case for how “this” or “that” piece of the retail value chain is in dire need of analytics. However, the most compelling use case, particularly for brick-and-mortar retailers, aligns with how many are transforming their store network into an asset in this competitive market. During an NRF session last Monday, Mike Relich, COO of Lucky Brand, shared how his team approaches inventory challenges using artificial intelligence and advanced analytics to predict localized demand for optimized allocations and store fulfillment.
Mike Relich, COO of Lucky Brand, at NRF 2019 Big Exhibitor Session: "How Lucky Brand optimized allocation and store fulfillment with advanced analytics."
The inventory decisions you make in-season — be it during allocation or fulfillment — can make a huge impact on sales and margins, especially with the help of advanced analytics. Because in-season inventory decisions are made much closer to the point of purchase, more relevant data accumulates, enabling predictive tools to more accurately detect opportunities for a full-price sale. Countless optimization scenarios come into play while meeting localized demand, and it's up to retailers to ensure inventories are as productive as possible.
As an example, Lucky Brand’s approach to optimizing store fulfillment is based on leveraging AI to identify stores with less demand and slower foot-traffic. In doing so, they are able to “fulfill 40% of e-commerce orders from stores and even direct e-commerce fulfillment based on store inventory” to ultimately increase store throughput, while reducing fulfillment costs (i.e., split shipments, shipping costs, etc.)
That way, Lucky can consider potential demand for a product at a particular location when making in-season inventory decisions. While you can't undo the inventory decisions made pre-season, but you can make the most of those investments in-season by optimizing the placement and fulfillment of product across stores.
It's Time to Embrace AI for Optimization
“There has never been a more important time to embrace analytics, AI, transparency and speed into problem solving for the most critical initiatives.” - Galagher Jeff, VP of Walmart, NRF 2019
There's a clear sense of urgency coming from the BIG show in NYC last week around utilizing advanced analytics and predictive tools to help retailers proactively drive business decisions. The traditional, run-of-the-mill merchant is evolving into a "digital merchant," as Galagher Jeff, VP of Walmart, expressed during a session at NRF. This means moving away from gut-driven, reactive, and siloed decision-making to data-informed, predictive, and customer-centric decisions.
With this in mind — along with the state of retail given the rise of e-commerce, changing customer expectations, and new competition — it's time to experiment with advanced analytics to ensure inventories are optimized to meet customer demand.