From generic browsing to personalized discovery: 28% higher cart values through machine learning-driven recommendations

FashionAI, a fast-growing online fashion retailer with 500,000+ monthly visitors, struggled with a critical challenge: despite offering thousands of curated fashion items, customers found it overwhelming to discover products that matched their unique style. The generic browsing experience led to high bounce rates, low engagement, and frequent cart abandonment. Traditional rule-based product recommendations were simplistic and ineffective—showing items based solely on category or price range without understanding individual preferences, body types, or style evolution. Customers spent excessive time searching through irrelevant products, often leaving frustrated without making a purchase. Meanwhile, valuable inventory languished undiscovered in the catalog. With fashion being highly personal and trend-driven, FashionAI needed a sophisticated solution that could understand nuanced style preferences, recognize emerging trends, and adapt to individual customer journeys in real-time. The company required an intelligent recommendation engine that would transform browsing from a chore into an engaging, personalized discovery experience.