CatalogSignalSee where your catalog can pull ahead and win more AI shortlists.
The CatalogSignal CEI Benchmark tracks 100 brands across 10 retail verticals to show where category leaders pull ahead, where the clearest opportunities sit, and which improvements can create durable separation.
100 brands. 10 verticals. One governed view.
The benchmark compares brands across 16 signals and five pillars: Foundation, Differentiation, Retrieval, Integrity, and Authority. Every ranked result is backed by measured evidence.
Brands across the active benchmark registry.
Retail verticals in the governed panel.
The common measurement structure.
Ten retail categories shaping AI-led shopping.
Beauty and Cosmetics, Consumer Electronics, Drugstore and Discount Retail, Fashion and Apparel, General Merchandise and Marketplaces, Home and Furniture, Home Improvement, Jewelry and Accessories, Pet Supplies, and Sporting Goods and Outdoor.
See what AI can find, understand, and support across the category.
Each release combines crawl and catalog evidence, public or authorized inputs, local retrieval tests, reviews, authority sources, video evidence, and responses from OpenAI, Anthropic, Google Gemini, and Perplexity.
Every result preserves the provider, model, query sample, access path, product-level evidence, failures, and held measurements behind the score, making category comparisons traceable and actionable.
A fair, consistent comparison across every ranked brand.
Sixteen signals roll into five pillars and the 0 to 100 CEI, giving every brand the same clear structure for understanding strengths and prioritizing improvements.
A governed common basis and compatible methodology era keep every ranked comparison consistent, so teams can focus on the category patterns and product-level improvements that matter.
From category position to competitive action plan.
Every result includes coverage, confidence, access mode, methodology era, and product-level evidence, giving teams the context to turn the benchmark into focused action.
Use the benchmark to spot category-wide patterns, see where leaders create separation, and choose the catalog improvements most likely to strengthen AI discoverability.
Turn category position into recommendation advantage.
See what leaders do differently, focus the work that can create separation, and move more products onto the AI shortlist.
Get a preliminary catalog check