Listening and talking to many retailers, most traditional “nonessential” companies report they have lost close to half of their revenue in the first half of this year due to the impact of COVID-19. Given the temporary closing of stores and the slow reopening, revenues will continue to be a challenge, as will cash flow. Current estimates from the Federal Reserve and others expect revenues will come back to 75% to 80% of their pre-pandemic levels for these retailers, but that may take months or, in some cases, even more than a year.
The silver lining is that many retailers did not see further revenue erosion, due, in no small part, to the increases in online purchasing. Many companies reacted very quickly during the pandemic to capture sales by adding omni capabilities around delivery, or pickup in store or at the curb. However, even with that revenue, many nonessential retailers are experiencing significant reductions in margins, over 20% in some cases, according to Global Data. Why? The simple answer is they are not ready to for a true omnichannel retail environment.
Before the pandemic, only a few forward-thinking traditional retailers had more than 20% of their revenues from online. Others lagged further behind in e-commerce penetration and the operational efficiencies needed to operate that business. So, when online business grew by as much as three or four times during the pandemic, many retailers experienced significant labor and shipping cost increases to fulfill those orders. Inventory was not in the right location, labor was disrupted and, in many situations, orders led to split shipments from separate locations.
After the pandemic started, healthy retailers, as evidenced by their earnings calls, are seeing their online sales settle down to 40% to 50% of their total revenue. Meaning, even if a retailer had a strong online channel before, they still need to up their game to be ready for a significant penetration of online demand if they don’t want to see margins erode away. And for those retailers that had not yet fully developed their online presence, they have even farther to go.
At the start, when retailers were preparing to go online, most invested in the front end — i.e., designing their website, building an app, and building on-site recommendations and reviews. They concentrated, rightly so, on purchase experience and competing against other e-commerce sites specifically because the first step was to acquire online customers.
However, to maintain and/or grow margins, especially in this new retail world, the focus needs to shift to the back end. Companies need to start from the initial buy — they need to curate the assortment differently, because online demand can be very different from the traditional store channel. Then they should move onto product placement — stores should carry products that satisfy not only store customers but ZIP code-level online demand as well. Finally, retailers move on to pricing — knowing the opportunity when the online demand can eliminate the need for store markdowns to generate margins. In summary, retailers need to start thinking “omni-first.”
As a simple illustration, consider a retailer that carries sporting and fitness gear. For specialized sports, such as lacrosse, they know some stores do not sell much of those products, but they still tend to stock a bit either to show off the breadth of their product mix to their heavy in-store traffic or simply because it is easier for the planners not to differentiate between stores. But, by using predictive analytics over product attributes, a retailer can now be smart about carrying lacrosse equipment in selected stores in competitive markets or where there is high demand, fulfilling other customer orders straight from the distribution center.
With the insight into where lacrosse-related product will likely be purchased, in-store and/or online, a merchant can make very different purchasing decisions. They can, for instance, buy more of some items and less of others, different colors, sizes and packs — knowing that they won’t need extra “display” product in some stores and relying more on shipping direct from the DC, especially for the increase in online orders.
To anticipate demand, retailers must start by using more than Excel-based or operational tools that simply repeat historical buying behavior or respond too slowly to the market signals. In the new omni-first environment, every online customer order can be fulfilled from a different store or distribution point and online order returns can be made to any store, thus impacting the decision on how each shipping location should be allocated or priced for the ordered or returned product. To understand the shifts in demand quickly, retailers need to invest in predictive, AI-based tools. When they know demand, they can also anticipate when to promote or mark down, by individual store, to capitalize margin on each customer purchase — in-store and online.
Taking the sporting and fitness gear example one step further, perhaps that same retailer has an online customer order for lacrosse merchandise late in the season — headgear, pads, cleats and an equipment bag. The distribution center no longer has the right size cleats, the closest store has the cleats but has enough demand that they haven’t marked down their prices yet, and another store 1,500 miles further away (with higher shipping costs) has everything and has already significantly marked down their product for end of season closeout.
Most retailers’ fulfillment systems would likely end up issuing multiple shipment requests — breaking the order between the DC and closest store. The problem is the retailer could be giving up margin by (1) paying for multiple shipments when it could be sourced from one location and (2) utilizing the inventory from a location that can still achieve better margin on their product. What is needed is a fulfillment algorithm that looks at the costs of all combinations.
So, going back to the lacrosse online order example, with the right solution in place, the order would be immediately routed to the store 1,500 miles away. Why? The system would do the analysis to realize that first, it could fulfill the entire order in one shipment, saving on labor costs. Then, it would see that the product in that store was already marked down and the tradeoff of potential higher shipping costs would be offset by being able to sell the product at full price at the closer store. All this would be decided by the AI solution as soon as the online order was received without requiring human intervention.
Retailers need the right AI tools that can optimize allocation, pricing and each order fulfillment request, which in turn helps them optimize inventory and protect margin. Retailers that can anticipate, plan and execute effectively in an omnichannel world by making automated assortment, pricing and fulfillment decisions can buy less, improve sell-through and reduce discounting — while, at the same time, grow revenue and improve customer satisfaction.
Yogesh Kulkarni is executive vice president of marketing and pricing analytics and leads the Marketing & Pricing Analytics business unit at Antuit. He can be reached at firstname.lastname@example.org.