AI-driven personalization typically deploys some combination of machine learning (ML), natural language processing (NLP) and generative AI. Generally, the process works by collecting customer data about user behavior, preferences and interactions—along with contextual data like location, time of day and device used. Often, this data-collection involves merging organizational data with third-party datasets.
This data is then analyzed by AI algorithms, which identify patterns and trends in user behavior. Typically, the AI will also group users into segments based on similar characteristics and behaviors in a process known as audience segmentation. By analyzing these segments and user behaviors, the AI then recommends products, services or content that aligns with user preferences and demographics. It can also display specific content on a website or app to different users based on their unique profiles.
As the AI continues to “learn” from users over time, it further optimizes its personalization process, adapting continuously to refine its recommendations and responses.