How to Accelerate Your E-Commerce Strategy by Years and Get on the Path to Profitability
In e-commerce, speed and order accuracy are the key drivers of customer satisfaction. A customer who expects diapers to be delivered in half an hour may abandon a retailer forever after one messy delay or mistake.
Brick-and-mortar retailers that are building out their e-commerce operations are feeling pressure on many fronts. Q-commerce companies, which promise 15- or 30-minute deliveries, are heightening customer expectations for almost instantaneous deliveries.
Speed and accuracy are expensive to provide, however. And passing on the cost to customers is problematic. A McKinsey study found half of omnichannel consumers will shop elsewhere when delivery times are too long — but only one in five will accept even a marginal increase in shipping fees for faster delivery.
In this rock-and-a-hard place retail environment, the pathway to profitability must include more accurate and granular product location information within stores.
The Problem With Order Pickers
Just a few years ago, many retailers considered moving some or all e-commerce picking operations out of their stores to a micro-fulfillment center (MFC) or a central fulfillment center (CFC). In-store, they were creating sub-optimal customer experiences and generating too much traffic and congestion in aisles.
Pandemic-related changes in consumer behavior put the brakes on that type of thinking. Last Black Friday, in-store traffic was down 28.3 percent from pre-pandemic levels, while e-commerce sales grew 11 percent. By 2026, more than 20 percent of U.S. grocery sales are expected to be from e-commerce, double the amount today.
Most fulfillment centers take an average of four hours to eight hours to pick, pack and deliver an order, McKinsey found. In-store picking, which brings the goods closer to the customer, became essential as e-commerce surged to keep pace with consumer demand and expectation of speed. .
However, stores generally have inventory accuracy rates of 70 percent to 90 percent — far below the 99.5-plus percent of most distribution centers, McKinsey determined. Furthermore, in-store picking typically costs 1.5 to two times more on a cost-per-pick basis.
The top expense in e-commerce today is the labor cost associated with shopping for an order, often expressed in the industry parlance of total “units per hour” (UPH) that can be processed by a facility, pack-and-pick lines, and other variables.
That cost of labor is rising with a worker shortage that's driving wage hikes and higher healthcare costs. In addition, surging oil prices are bringing higher transportation costs.
How quickly and efficiently associates can pick items in-store will determine e-commerce profitability. At Kroger, pickers are tasked to retrieve each item within 30 seconds (120 UPH) and to find 95 percent of a customer’s grocery list, according to a New York Times story.
Achieving these goals is a challenge, as the Times described in the experience of one picker: “Waiting in line at the deli counter and being stopped by customers asking for help would slow him down, and he dreaded lists with seasonal goods, like Christmas treats, because [he would typically be directed] to the wrong aisle. If an item was out of stock, his fulfillment rate was dinged.”
Out-of-stocks and errors attributable to the mental stress and pressures pickers face also bring costs that ripple through the supply chain.
Better Inventory Data
Planograms provide little help. Because they're so costly to create, many large retailers use universal planograms that don’t match the particularities of specific stores. A store that's supposed to have 120 feet of beverages may, in reality, have 60 feet in one aisle and 60 feet in another aisle.
As a result, most retailers today have no idea if the item they sold to customers online is actually in stock in a store. Perpetual inventory data is highly inaccurate. Therefore, retailers have to surface every item in their catalogue online, another inefficiency compared to Q-commerce companies that offer only a rationalized subset of the items offered by brick-and-mortar retailers.
The consequences can be dramatic: the average retail associate spends triple the time searching for items that aren’t actually available in-store, creating an unhappy customer in the process.
By leveraging modern technologies such as artificial intelligence (AI), retailers can not only accurately identify in near real time, update their item catalogue to what's actually stocked at an individual store, but also update what aisle a product is in and what specific shelf or section it's in on the aisle. If an associate knows a bottle of ketchup is on the third shelf from the bottom, and the third item on the left, precious time is shaved off the picking task. UPH — and profitability — go up. Even a small improvement in location accuracy translates into a huge savings for a large organization.
Many Potential Benefits
Savvy retailers are using near real-time product location information to not only improve in-store picking, but to power guided shopping trips for customers at brick-and-mortar locations. Now customers, too, can tap their phone and find the exact location of the product they want. At Kroger, customers can use the retailer's app to locate the aisle of a product, but not the granular location.
And when the customer engages with a retailer digitally, the retailer enjoys a higher share of wallet. Customers often will add to their cart items they might have picked up at other retailers when they were shopping at physical stores more routinely.
Retailers have taken steps to monetize customer loyalty data, perpetual inventory information, and sales data. Shelf data has been left out of the mix. Now that the pandemic has turned 10-year e-commerce plans into five-year and two-year plans, it’s imperative for retailers to take the next step with near real-time, highly accurate shelf inventory data.
Filling orders represents a huge cost. But retailers must do it or die. They must move with the customer, but do so in a profitable way. There has never been a time when it’s been more important for retailers to create efficiencies in their picker operations — and that can only happen with more precise product location information.
Mark Abernathy is head of retail at Pensa Systems, a syndicated data solution that drives growth for CPG brands and retailers through accurate and actionable shelf visibility.
Mark Abernathy serves as Pensa Systems’ head of market development for the retail sector. In this role, Mark leads Pensa’s go-to-market efforts to meet retailers’ growing demand for automated real-world shelf visibility data. He previously served as head of eCommerce operations at Kroger, where he created and led a profitable $6B dollar business across 2000+ locations in 19 regional divisions and expanded operations to support new lines of business including Home Delivery, Ship-to-Home and Click&Collect. Earlier in his career, Mark spent 13 years at Harris Teeter in a variety of store operations roles. He brings a wealth of expertise in helping retailers accelerate growth, improve customer satisfaction and manage disruptions.