The pandemic has had long-lasting effects on the supply chain. Product demand is up, e-commerce shopping has increased, and more consumers are returning items purchased online. To cope with these trends, many retailers are over-ordering and accelerating their order and re-order lead times. 

In 2021, PBS found that a pair of shoes took 80 days to get from Asia to retailers in North America — double the time it took pre-pandemic. Around the same time, a Utah-based retailer reported inventory levels of just 55% compared to normal, all due to freight delays.

One key to helping retailers mitigate supply chain issues today is a product-discovery platform driven by artificial intelligence. Through the use of features made possible by AI and machine learning, retailers can maintain a high-quality user experience by delivering quick, personalized and relevant search and recommendations results. In the process, customers can still purchase products that meet their needs and are in stock.

In Deloitte’s 2022 Retail Industry Outlook, 80% of executives surveyed said consumers will prioritize stock availability over brand loyalty, driving home the role that product discovery can play in reaching sales goals. If the exact product isn’t available, appropriate items must be provided as alternatives to maintain the sale.

The search bar is where the customer journey begins. Whether they complete the purchase online, or use an e-commerce site to research a product before purchasing in-store, nearly half (40%) of consumers start their search online.

A search experience that isn’t up to a customer’s expectations is the quickest and easiest way to lose a sale. A recent report from Google found that approximately $300 billion in sales are lost in the U.S. each year as a result of bad online search experiences.

The impact on the relationship with the customer can extend well beyond one bad visit. The Google report found that 85% of online shoppers view a brand differently after experiencing difficulties with product search.

The revenue lost from poor search experiences, and the potential revenue available from good ones, makes investing in a product discovery application a priority for retailers. The right search technology can help retailers provide up-to-date search results to consumers looking to purchase a product online or research their in-store purchasing options.

Also in the Google report, 90% of consumers said that an easy-to-use search function is essential when shopping on a retail site. AI-powered product discovery platforms can understand customer intent, making search and recommendation results that are hyper-personalized to each individual shopper.

The tool takes into account factors such as inventory availability and the likelihood of an item to be restocked quickly. AI search can account for variations in inventory, and tell the customer when stocks of a given product are low. It can also remove out-of-stock products from the search results, instead providing smart replacement recommendations that still fulfill the customer’s search goals.

AI search uses pre-existing data and analytics to boost new products that might otherwise lack the ratings and purchase history needed to organically move them to the top of search results. Without next-generation AI technology, the task can only be accomplished through legacy systems and human intervention, which is more time-intensive, overly reliant on a technician’s skillset and vulnerable to error.

AI search can be seamlessly integrated into any e-commerce platform or supplemental applications in a retailer’s tech stack. This means that retailers can own and develop the front-end search experience for customers, while the headless, API-based architecture provides powerful capabilities behind the scene. It also enables the development team to build front-end experiences that can be embedded into the user interface of each customer segment.

Modern-day product discovery platforms in support of e-commerce can learn to consider such factors as inventory levels, customer reviews and search behavior. AI and machine learning technology improve the retailer's back-end experience, managing inventory that ties into the customer’s search and recommendations experience. At the same time, it creates a high-quality, customized experience on the customer’s end, resulting in higher conversion rates.

Roland Goassage is chief executive officer of GroupBy.