Navigating the Challenges of AI-Driven Product Discovery

As AI tools like ChatGPT become embedded in consumers’ shopping journeys, the mechanisms governing product discovery are increasingly shaping retail success. Currently, ChatGPT tends to prioritise results from high-authority, large-scale marketplaces. This algorithmic preference inadvertently marginalises smaller and niche retailers, even when they stock precisely what consumers are looking for.

How ChatGPT Currently Approaches Product Discovery

When users query ChatGPT with requests like “grey snake-print midi dress” or “light blue outfit for a beach wedding”, the current retrieval system typically operates in two distinct phases:

1. Initial Sweep: ChatGPT conducts a broad web query using keywords, often picking up top-ranking fashion publications and major retailers.
2. Drill-Down Phase: ChatGPT separately queries "high-yield" websites, such as ASOS, Revolve, Nordstrom, to surface reliable, structured, and editorially curated results.

These refined queries become inscreasingly specific. , ranging from “pastel blue beach wedding guest dress”, to “sky blue linen midi dress wedding guest coastal ceremony”, “beach wedding guest outfit light blue linen jumpsuit”. Results from these queries are merged and ranked based on overall relevance and domain authority. This leads to the aforementioned overrepresentation of established brands, creating substantial barriers to entry for smaller labels.

Technical Details

• Initial queries use semantic expansions of user inputs, leveraging synonyms and related terminology.
• Subsequent queries explicitly restrict results to domains recognised for high availability and comprehensive product detail.
• Results are ranked primarily by domain authority, trust signals, product availability, and seasonal relevance.

The Future: Hybrid Relevance-Authority Ranking

Don't attempt to game the system

The era of keyword stuffing and artificial SEO inflation has effectively ended. Techniques designed solely to manipulate the system will fail in the context of AI-driven discovery. Retailers must focus on genuine, descriptive, and authentic product information to succeed in this new era.

Strategic Actions For Retailers

• Implement structured data (Schema.org/JSON-LD) consistently across all product pages.
• Publish XML sitemaps and maintain clearly structured product pages.
• Participate in aggregator or affiliate networks to expand visibility
• Consider controlled API integrations via OpenAPI specifications.
• Ensure all product attributes and descriptions are clear, accurate, and written for humans, not algorithms.

The Critical Importance of Accurate Product Labelling in Natural Language

Accurate, human-readable product labeling has become business-critical. AI-driven tools rely heavily on natural language processing, meaning clear, authentic descriptions significantly enhance product discoverability. Research demonstrates that products with comprehensive, natural-language attributes are up to 3x more likely to surface in AI-driven searches.Retailers must ensure products are labeled correctly with accurate occasion, trend, aesthetic, colour, and style attributes.

Your engagement directly influences the development priorities, ensuring improvements that benefit both users and retailers alike. Together, we can shape a future where AI product discovery is efficient, equitable, and economically sustainable.

Mapp Fashion is your partner to make your product data and PDPs ready for AI-driven shopping.