Ranking Strategy & Relevance Overview
Ranking strategy and relevance tuning optimize how products are ordered in search results using LLM based ranking and relevance, personalization, and multi-objective optimization to balance conversion, revenue, profit, and customer experience.
Key Capabilities
Ranking strategy enables you to:
- LLM Based Ranking and Relevance: LLMs trained specifically on your customer data with controls to optimize for conversion, revenue, margin, and other business criteria—learning patterns that apply across your catalog even for new products with no sales history
- Intent Handling: Detect query intent (brand vs category vs attribute vs use-case) and apply appropriate ranking behavior
- Personalization: Per-user re-ranking using history, affinities, price sensitivity, and size/color preferences
- Multi-Objective Optimization: Balance conversion, revenue, profit margin, inventory, and customer experience
When to Use Ranking Strategy
Use ranking strategy when you need to:
- Optimize for business metrics (revenue, margin, conversion)
- Personalize results for individual users
- Handle different query intents appropriately
- Balance multiple business objectives
- Promote new products without sacrificing relevance
- Learn from user behavior to improve rankings over time
Related Topics
- LLM Based Ranking and Relevance - LLM-powered ranking trained on your customer data
- Personalization - User-specific ranking
- Intent Handling - Query intent detection and handling
- Multi-Objective Optimization - Balance multiple goals