Paul Boyle, CEO at Retail Insight, shows the answer lies in understanding and acting on the data.
Food retail demand has become fractured across multiple channels, with traditional bricks-and-mortar formats losing out to the online medium. In 2019, online grocery accounted for 6.3% of total US grocery sales. Then in 2021, grocery e-commerce sales reached 9.5% and are predicted to increase to 11.1% and then 20.5% in 2022 and 2026 respectively. The sign is clear – e-commerce is here to stay.
The impact of this increasing growth is the resulting strain on incumbent infrastructure that was originally designed to manage only about 5% of a retailer’s total sales capacity; the steady rise in costs from stretching this capacity has shown home delivery, Buy-Online-Pick-Up-In-Store (BOPIS) and curbside fulfillment to be highly unprofitable. Research by Bain & Co. shows that manually picking online orders from a store and delivering it for free has a typical operating margin of -15%. Whilst, free-of-charge, BOPIS store orders are less costly but still come in at -5%.
Alongside the infrastructure dilemma, when speaking to grocers about their online operation, it becomes clear just how challenging optimizing and unlocking value during the e-commerce end-to-end process can be. Whether that be through the cost of poor substitution choices, online ranging availability limitations, or general omnichannel performance diagnostics – retailers all face similar issues, all of which Retail Insight has been working to solve with our retail customers through the use of data.
Increasing demand is compounded by the continuing wage rises, as labor scarcity increases retailers are finding themselves in a battle for talent – leading to broader benefits packages and rising wages being offered. The US Bureau of Labour Statistics states that the average hourly earnings of all employees in the US working for private companies rose to $30.85 in September 2020 after significant rises across the year. This means a store picking model that is already unprofitable is leaking further margin.
External pressure is intensifying as innovation comes to the market from emerging competitors that are challenging cost bases further, meaning traditional retailers are needing to absorb even more costs to keep pace. Consider Amazon’s recent investment in an ultrafast delivery player, Deliveroo – similar to the likes of Getir and DoorDash. While there is a range of opinions as to Amazon’s motivation, it is clear that the e-commerce giant will aggressively pursue its goal of acquiring new customers and gaining access to them through new channels. This investment also complements their commitments to rapid delivery.
For grocers’ customer base, the increasing appeal is to adopt a more omnichannel approach to consumption – a sign of the epidemic of increasing customer disloyalty. According to FMI in its ‘U.S. Grocery Shopper Trends 2020 report’, grocery shoppers now visit 5.1 grocery stores per month, up from 4.4 in 2019 while Millennials and Gen Z visit 6 on average monthly.
Altogether, these may seem like the doomsday scenario for the retailer in the bricks vs clicks battle, but in fact, they are an opportunity for grocers to focus on in-store picking efficiency and profitability. This can seem like a very large mountain to climb because of the many moving parts in an e-commerce picking operation but as mentioned previously, there are some standout pain points that can be addressed, and quickly.
For instance, at Retail Insight we have worked with several US grocers on the challenge of Nilpicks, this being the time involved in a store associate looking for an item that is not available for picking. Further time is then lost as the store associate looks for a substitution. The issue is compounded by the suboptimal nature of many of these substitutions as customers are typically unhappy receiving anything less than what they ordered. In this case, simple integration of known out-of-stocks to the picking process would direct shoppers to a clearer and less disrupted shop. Through doing such a measure, one retailer saved $24m a year in labor costs.
For retailers that want to prove the business case to scale, it makes sense to analyze the results across a number of stores to assess the size of the prize. As part of the pilot, it is also recommended to extend the algorithm to include more store signals, levers, and drivers to generate a larger set of clusters for tuning at a lower granularity. And finally, review the clusters to see if they trigger upstream actions in the supply chain such as replenishment or direct customers to the substitute at the point of order by removing likely Nilpicks from the website.
This is just one example of how the concept of looking for connections between data points to solve everyday challenges can produce dramatic results, but this is only the start. Going through this review process should be the template for uncovering further ways to improve the operational efficiency of in-store picking.
This article was originally featured in Retail Touchpoints.