June 24, 2021
By Srini Venkatesan, Executive Vice President, Walmart Global Tech
Last year, amidst the pandemic, Americans’ shopping behavior shifted radically. Customers increasingly began shopping online for everyday needs, including food and groceries. That surge in demand was a boon for online grocers, but it also presented a unique challenge to retailers as the combination of in-store shoppers and online volume meant some popular items could quickly sell out. Walmart’s solution was to use artificial intelligence to help both customers and Personal Shoppers choose the best substitute for an out-of-stock item.
For example, imagine you’re a Personal Shopper looking for cherry yogurt for an online grocery order. But when you get to the yogurt aisle, there’s no cherry yogurt left. You see strawberry, raspberry and blueberry yogurt — would the customer like one of those options? Perhaps another flavor, like vanilla? Fat-free? Skip the yogurt all together? How can a Personal Shopper decide which is the next best option for a customer they may never have met?
The decision on how to substitute is complex and highly personal to each customer. If the wrong choice is made, it can negatively impact customer satisfaction and increase costs. In the past, our Personal Shoppers would go through a manual process to determine the best way to manage a substitution. But there are nearly 100 different factors that can go into that decision. Trying to account for all of these would not only be too difficult, but it would also be incredibly time consuming.
The decision on how to substitute is complex and highly personal to each customer. If the wrong choice is made, it can negatively impact customer satisfaction and increase costs.
To help ensure a substitution that will result in a happy customer, our team created a technology solution to help identify the next best item for customers if an item they selected is out of stock.
The tech we built uses deep learning AI to consider hundreds of variables — size, type, brand, price, aggregate shopper data, individual customer preference, current inventory and more – in real time to determine the best next available item. It then preemptively asks the customer to approve the substituted item or let us know they don’t want it, an important signal that’s fed back into our learning algorithms to improve the accuracy of future recommendations.
The solution is also designed to make our associates’ jobs easier. Instead of having to guess, the Personal Shopper can be told precisely what the customer may prefer. If our Personal Shoppers are preparing orders and come across an item that is not available, our system suggests the alternative product. Our tech even shows our Personal Shopper where the item is located in the store, simplifying the decision-making process for our team and enabling them to prepare orders quickly and efficiently.
Following the deployment of this tech, customer acceptance of substitutions has increased to over 95%.
We continue to iterate and enhance this technology, and our customers are responding positively. The best part is, throughout the entire process the system is learning and getting smarter based on customer input and actions. Our goal is never to be out of stock and never to have substitutions. But, when it happens, the technology we’ve built helps ensure customers get the next best thing.
