Industry · Ecommerce brands
AI Visibility for D2C and Ecommerce Brands.
When a buyer asks ChatGPT, Gemini, Perplexity or Claude: "best [product category] brand for [use case / value]", is your firm in the answer? For most, the answer is no.
Exclusion rate
91%
of firms in our audit data are not named in the relevant AI answers.
Buyer query shape
"best [product category] brand for [use case / value]"
Time to first measurable lift
30 days
From baseline to first re-measurement.
The failure pattern.
Brand sites optimise for product pages and paid acquisition. AI shopping assistants recommend at the brand and category level using structured product data, brand stories and corroborated reviews.
The four levers
How we engineer selection in ecommerce brands.
Brand-level entity definition
Define the brand as an entity with clear values, materials, manufacturing and price-tier signals AI shopping models can read.
Structured product data
Schema, attributes and category alignment that AI systems can ingest cleanly across all product pages.
Editorial corroboration
Construct presence in independent editorial: best-of guides, sustainability registers, materials databases, trade press.
Review-platform parity
Audit and align brand presence on Trustpilot, Reviews.io and category-specific review sources.
From the field
AI-assisted shopping research is the fastest-growing segment of ecommerce discovery. Brand-level inclusion compounds across every product query.
Common questions.
Is this just shopping schema?
Schema is necessary but not sufficient. AVO addresses brand entity, corroboration and editorial signal alongside structured product data.
Does this affect Amazon visibility?
Indirectly. AI shopping assistants increasingly read across marketplaces and brand sites. Brand-level signal strength influences both.
Find out where you stand.
A free AVO Score™ on the actual buyer queries that matter in your practice.
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