Personalized Product Recommendations

This application area focuses on dynamically recommending products to each shopper based on their behavior, preferences, and context, rather than relying on static, rules-based lists like “bestsellers” or generic cross-sells. It analyzes data such as browsing history, past purchases, items in the cart, and real-time session signals to surface the most relevant items, bundles, or offers for every individual across web, app, and messaging channels. It matters because product discovery is a key revenue lever in retail and ecommerce. Personalized recommendations increase conversion rates, average order value, and customer lifetime value by making it easier for shoppers to find items they’re likely to buy. AI techniques enable this personalization to happen at scale for thousands or millions of customers, continuously learning from new data and outperforming manual merchandising rules that quickly become stale or misaligned with each shopper’s real interests.