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.
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.
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.
