Traditionally, consumer data has been drawn from sources such as demographics, search queries, browsing behavior and purchase history, yielding broad, generic product recommendations and offers.

​Going forward, retail brands have the potential to use AI, machine learning and big data analytics to gain deeper insights into consumer preferences and behaviors by parsing much richer consumer information: social media posts and reviews, customer service sessions with smart chatbots, product interactions in AR/VR environments and even in-store product evaluations. 

With this intelligence, companies can gain a deeper, more granular understanding of consumer motivations, values and intent. Generative AI can then use these insights to predict emerging interests and unmet needs, informing customer-specific messaging, marketing, recommendations, pricing, promotions and incentives. These can be delivered precisely how, where and when each shopper prefers, creating a dynamic fit-for-me experience.