What type of AI is commonly utilized by eCommerce websites?

eCommerce websites commonly utilize recommendations as a type of AI because this approach directly enhances the shopping experience for customers. Recommendation systems analyze users' past behavior, preferences, and purchase history to suggest products that align with their interests. This personalization not only improves customer satisfaction but also increases conversion rates by making it easier for users to discover items they may not have found otherwise.

By leveraging data-driven insights, recommendation systems can predict items that a customer is likely to buy, fostering engagement and encouraging more purchases. Techniques such as collaborative filtering, content-based filtering, and hybrid approaches are often employed in these systems to provide relevant suggestions.

While clustering algorithms, natural language processing, and deep learning systems play significant roles in AI and machine learning, they are typically components or methodologies that support various functions. For instance, clustering can analyze customer segments, natural language processing enhances customer interaction with chatbots or searches, and deep learning can improve image recognition or more complex predictive analytics. However, the core function of driving direct retail engagement and enhancing user experience through tailored suggestions clearly establishes recommendations as the most prevalent AI application in eCommerce contexts.