As the ecommerce landscape becomes more competitive, top retailers like Amazon stay ahead of the curve by delivering a compelling product experience to their customers. This is made possible by powerful AI algorithms that deeply understand the customer’s needs and create a personalized shopping experience. Here are five essential AI-powered features for modern ecommerce that all retailers will find useful.
1. Personalized search and recommendations.
Providing personalized search results and product recommendations is critical to ensure a frictionless shopping experience for a range of customers — from those searching for specific products to the ones exploring department pages to discover new products.
In all scenarios, AI models can show highly personalized and relevant products by analyzing a customer’s search queries or browsing behavior in the current shopping session and combining them with features like the customer’s taste preferences and purchase patterns accumulated over a long period of time. AI models can also instantly build taste profiles for new or guest users during the current shopping session, quickly personalizing the shopping experience even on the customer’s first visit.
These models can be optimized for multiple objectives like driving more revenue per order, increasing long-term satisfaction, compelling the user to return more often to the website, incentivizing the customer to sign up for subscriptions or premium services and more. For example, Amazon is good at incentivizing customers to sign up for its Prime membership program by recommending highly relevant “Prime-only” products, which tend to have steep discounts and free shipping.
2. Personalized website presentation.
Dynamically changing the layout of your website and presenting it in a personalized way to the user is a great way to drive long-term user satisfaction and increase the likelihood of a customer returning. It helps prevent customers from feeling overwhelmed when trying to navigate a large selection of products.
Through the use of AI models, it is possible to predict a user’s shopping intentions when they arrive on a website and then present a personalized layout that makes it easier for them to make purchasing decisions. This can help reduce friction and encourage users to make a purchase. For example, an apparel website can dynamically generate a personalized collection of products based on the season, gender, age and location of a user.
Similarly, AI algorithms can select images that are more likely to appeal to a user’s preferences or automatically generate badges like “eco-friendly” or “small business” based on past purchasing patterns. These features are highly effective in encouraging a user to make a purchase.
3. Chatbots for customer service.
Leveraging chatbots in conjunction with live agent support can help in a wide variety of tasks, ranging from answering common questions about products to helping customers navigate complex purchase decisions. This concierge-like experience helps customers make decisions faster and saves a lot of time and money for businesses on customer support. A recent survey from Capgemini found that 74% of customers have already used voice/chat interfaces for researching products or making purchase decisions.
These chatbots are powered by AI models that are built using data from real-life conversations. They can deeply understand natural language sentences and generate human-like responses. These models also use the context of the entire shopping session to better understand the customer’s intent and generate responses with a high degree of accuracy.
Furthermore, algorithms that underlie modern chatbots like ChatGPT are capable of conversing in multiple languages, thus expanding the reach of a business to untapped customer segments.
4. Fraud detection.
Ecommerce fraud can happen before, during and after the purchase, and result in severe revenue loss to many online merchants. Preventing fraud can be difficult without implementing multiple security measures, which may cause inconvenience for legitimate customers during their shopping journey. AI models help address this problem by detecting fraud with a high degree of accuracy in real time and enforcing the right set of interventions to prevent it.
This is achieved by encapsulating many signals that are indicative of possible fraudulent activity, such as shipping addresses being shared by multiple accounts, the same user creating multiple orders in a short amount of time, repeated failed transactions from the account and more.
Given the complex nature of the problem, AI models are used in conjunction with a bespoke “rules engine,” which encodes many heuristic rules to detect well-known fraud patterns. For example, the predictions of the AI model can be used for enforcing soft actions like disabling the user’s account for a few hours, while the rules engine’s output can be used for stronger decisions like banning a user.
5. Personalized price optimization.
AI models can help predict a user’s price preferences, available budget, affinity for deals and discounts, and help ecommerce businesses make targeted pricing decisions for each customer. This data can be used to dynamically change the price at which a product is offered, which in turn helps with secondary outcomes like driving more revenue per order, encouraging a new customer to make their first purchase or preventing a customer from churning.
These models are built using data such as a customer’s past purchase history, income range (gathered from third-party sources), aggregate statistics from similar users such as those from the same zip code, the category of the item being purchased, product seasonality, price of the same product offered by a competitor, margin made on the product and more.
Modern ecommerce websites can greatly benefit from the incorporation of AI-powered features. These features can improve the user experience, increase efficiency and productivity, and drive sales and revenue for the business. By implementing these AI-powered features, ecommerce businesses can stay ahead of the competition and provide a seamless and convenient shopping experience for their customers.
Tejaswi Tenneti is Director of Machine Learning at Instacart, the North American leader in online grocery. Prior to Instacart, Tenneti led machine-learning teams at Apple and Oracle where he worked on various applications related to search and recommendations for local maps data and enterprise. Tenneti holds a BS from IIIT, Allahabad and an MS from Stanford University specializing in AI.