Imagine a customer searching Amazon for "moisturizer for sensitive skin," only to find your product listing buried beneath countless competitors. The culprit? Traditional keyword-stuffing approaches that fail to meet the sophisticated demands of Amazon's Cosmo algorithm.
Amazon's search technology has evolved beyond simple keyword matching. The Cosmo system represents a fundamental shift toward semantic understanding - analyzing customer intent, purchase behaviors, and contextual relationships to deliver more relevant results.
From Keywords to Context: The Cosmo Difference
Unlike its predecessor A9, Cosmo doesn't simply match search terms to product listings. Instead, it builds comprehensive knowledge graphs by analyzing millions of search-purchase patterns, co-purchase behaviors, and other data points to understand the deeper motivations behind customer queries.
This semantic approach relies on 15 structured relationships that form the foundation of product understanding. By naturally incorporating these relationships throughout your listing - in titles, bullet points, images, and A+ content - sellers can dramatically improve both visibility and conversion rates.
The 15 Semantic Relationships That Power Product Discovery
1. Function & Usage: What Does the Product Do?
These relationships define a product's core capabilities and practical applications:
- used_for_func : Primary functions (e.g., "cleans pores," "removes oil")
- capable_of : Product capabilities (e.g., "keeps liquids hot for 12 hours")
- used_to : Specific actions enabled (e.g., "tightens screws," "assembles furniture")
2. Concept & Identity: What Is the Product?
These relationships establish categorical and conceptual positioning:
- used_as : Functional classifications (e.g., "fitness tracker," "health monitor")
- is_a : Product categories (e.g., "formal wear," "casual apparel")
3. Contextual Scenarios: When and Where Is It Used?
These relationships connect products to specific environments and situations:
- used_for_eve : Applicable events/activities (e.g., "beach vacation," "outdoor sports")
- used_on : Temporal usage (e.g., "dry winter months," "during sleep")
- used_in_loc : Physical locations (e.g., "bedroom," "office")
- used_in_body : Body applications (e.g., "facial use," "sensitive skin")
4. Audience & User Psychology: Who Uses It and Why?
These relationships define target users and their motivations:
- used_for_aud : Target demographics (e.g., "children 3-6 years")
- used_by : User identities (e.g., "cat owners," "pet lovers")
- xIs_a : User characteristics (e.g., "pregnant women")
- xInterested_in : User interests (e.g., "gardening," "DIY projects")
- xWant : User desires (e.g., "running," "hiking")
5. Complementary Relationships: What Works With It?
This relationship identifies product synergies:
- used_with : Complementary products (e.g., "phone case with screen protector")
Implementing Semantic Optimization
Effective semantic optimization requires natural integration across all listing elements:
- Visual storytelling that demonstrates usage scenarios (e.g., showing families using products)
- A+ content that highlights capabilities through both text and imagery
- Bullet points that address multiple semantic relationships simultaneously
Advanced sellers can leverage analytics to track performance across semantic dimensions, refining listings based on which relationships generate the strongest customer engagement and conversion rates.
This semantic approach transforms product listings from static keyword repositories into dynamic, intent-matching tools that speak directly to customer needs - the key to standing out in Amazon's increasingly sophisticated marketplace.