Imagine every shopper in your online store having a smart salesperson who remembers their tastes, budget, and goals, and quietly reorders the search results and product suggestions just for them every time they type in the same vague query like “running shoes.”
Generic search results treat all users the same and convert poorly, especially when queries are short, ambiguous, or intent varies widely (research vs ready-to-buy, bargain vs premium). This solution tailors search and recommendations to each user’s unique intent and behavior, increasing relevance, conversion rate, and basket size.
If implemented well, the moat comes from proprietary first-party behavioral data (clicks, purchases, session patterns), intent labels, and continuous feedback loops. Over time, the system learns a retailer’s specific audience, catalog, and language, making the personalization difficult for competitors to replicate quickly even with similar models.
Hybrid
Vector Search
Medium (Integration logic)
Real-time inference latency and cost for large traffic volumes, especially if combining vector search with per-user intent models and re-ranking at query time.
Early Majority
Focus on modeling granular, evolving user intent rather than just generic relevance, using behavioral signals and potentially LLM-based embeddings to re-rank and personalize search results per user-session instead of relying solely on keyword matching or static rules.