Mapp Fashion use cases are designed to support how shoppers discover products, understand them in context, and make confident decisions across on-site experiences and email. Each use case can be implemented independently, but all are built on the same underlying fashion logic, ensuring consistency across touchpoints.
At the core of every use case are Enriched Product Attributes. These translate standard catalog data into shopper-relevant context, such as fit, occasion, styling role, and intended use. This shared attribute layer aligns product discovery and recommendations with how customers actually browse and evaluate fashion, rather than how products are technically structured in a catalog.
Why This Approach Is Different
Many discovery and recommendation systems rely primarily on surface-level signals such as category, popularity, or co-purchase behaviour. While this approach can be effective in some retail contexts, it often falls short in fashion, where relevance depends on styling logic, context, and intent.
Mapp Fashion is built specifically for fashion use cases. Product understanding is based on:
Fashion-specific attributes that describe both physical and contextual characteristics
Styling logic that reflects how items are worn and combined
Consistent enrichment applied across the full catalog
Because all use cases rely on the same enriched product understanding, outputs remain coherent and aligned across channels, rather than feeling disconnected or purely automated.
From Product Data to Shopper Decisions
The use cases in this section support different stages of the customer journey while relying on the same underlying logic:
Product Discovery & Decision Support - Enriched Product Attributes, Personalized Outfit Recommendations, and Similar Items help shoppers find relevant products, understand how items fit into real-world use, and explore suitable alternatives when their first choice isn’t right.
Lifecycle & Email Activation - Product and Outfit Recommendations in BAU, themed, and post-purchase emails extend the same fashion context into CRM. This helps maintain relevance before and after purchase without requiring manual product selection.
Because enriched attributes and styling logic are reused across all implementations, shopper interactions in one area can inform and improve relevance in others over time, supporting more consistent decision-making across the customer journey.