Artificial intelligence has played a highly visible role in creating easier, more realistic buying experiences for online shoppers since the start of the pandemic. Virtual try-ons made it easier for people to buy clothing from home for Zoom meetings and leisure time, while 3D product previews have taken much of the uncertainty out of purchasing larger items like sofas and rugs.
However, AI also has a critical role to play in other aspects of customer experience, fraud prevention, and operations. As ecommerce and omnichannel retailers compete to deliver the best experiences and optimize their operations, here are some of the most exciting ways that AI can help.
Increasing order approval rates with AI
Approving as many good orders as possible is critical for revenue. It's also important for creating CX that makes shoppers want to return. Too often, online retailers err on the side of caution by automatically rejecting any order that has any kind of discrepancy, no matter how slight. So, for example, an order from a new customer might be rejected because they enter a ZIP code that's off by one digit from the Address Verification System record. Or they might reject an order from a returning customer because the order is placed from an unfamiliar device, like a new phone.
In addition to losing the profit from these falsely declined orders, businesses can lose the rejected customers. A March 2021 five-country online survey by ClearSale of more than 5,000 ecommerce shoppers found 40% will boycott a website after a decline. That means all the investment in marketing, including personalization, that went into bringing that customer through checkout is lost. So is the potential lifetime value of that customer.
What's more, 34% of declined customers say they'll post something negative about the store online, too, which creates hurdles to acquiring new customers.
AI can help prevent false declines and increase order approvals by using behavioral biometrics, historical customer data, and other information to quickly decide if an order is fraud or valid. When AI-screened orders are flagged as possible fraud, expert manual review is the next crucial step. By evaluating flagged orders and feeding the decisions back into the AI algorithm, the AI system gets smarter, the business approves more good orders, and fewer customers have a negative experience with the site or app.
Continuously segmenting customers for seamless personalization
Personalization matters to many online shoppers, especially younger ones. 18% of our survey participants between the ages of 18 and 24 said that "featured items picked just for me based on my shopping habits" would keep them shopping online rather than returning to brick-and-mortar stores. Meanwhile, just 11% of shoppers aged 65 and older said the same thing.
Not surprisingly, younger customers are more willing than older generations to share data in order to enjoy curated online shopping experiences. Businesses that cater to Gen Z shoppers and retailers that want to build inroads with this demographic need a platform for permissioning, collecting, analyzing, and deploying data for personalized interactions across touchpoints. However, consumers' habits and preferences change over time, and as we've seen since March 2020, they can change quickly.
To keep up with these changes and continue to offer accurately personalized offers in real time, retailers can use AI-powered customer data platforms that continuously analyze customers' online and in-store activity to adjust their segments as they age, change, and grow. With the right permissions, this data can also inform fraud prevention to further reduce the risk of false declines as customers' behavior evolves.
Managing inventory, logistics, and forecasting more effectively
Ecommerce inventory management has improved dramatically for many retailers since the pandemic began. At that time, the unknown duration of lockdowns and desire for safety drove many consumers to stockpile items online, while many retailers — especially in the grocery space — were quickly moving in-store inventories online. The result was often stockouts, post-order confusion, and disappointed shoppers.
Now, the top-performing retailers have their inventory visibility issues under control. Just as important, they have had time to establish separate operating models and inventory tracking systems for their online channels. Even if the in-store and online inventory systems are unified on one backend platform, having separate approaches for each channel supports better CX optimization. For example, if a retailer's ecommerce store relies on in-store inventory that's only updated a few times a day, the retailer risks disappointing online shoppers when items that were in stock at the time of order aren't actually available. Another inventory challenge is supply chain management, especially when manufacturing and transport centers around the world are dealing with outbreaks and staff shortages or lockdowns.
AI can help with inventory and logistics management by learning where and when demand is highest for certain products, where shipping and delivery bottlenecks are most likely to occur, and how retailers can move inventory from a region where there's little demand to one where there's more. Because of these advantages, the global logistics automation market is projected to grow by 12.4% CAGR through 2026.
Along the same lines, AI and machine learning can help retailers more accurately forecast demand, not only by analyzing retailers' past and current customer behavior in their channels but also by their searches and shopping across the web and apps. Improved forecasting can help stores get ahead of spikes in demand for certain products and avoid wasted spend on items that have declining demand.
AI is a major investment for e-commerce and omnichannel retailers, but one that can deliver ROI in multiple areas: higher order approvals, lower customer churn, less fraud, more accurate personalization, better inventory and logistics management, and clearer demand forecasts.
Together, all of these benefits set the stage for retail growth by creating a more customized, lower-friction, loyalty-building customer experience for online shoppers.