E-commerce & Retail · AI Search Visibility
A catalogue machines can read.
How do we get our products recommended by AI shopping assistants?
Short answer
Enrich product data with genuine structured attributes rather than marketing copy, so a system assembling a recommendation can match your products against real intent. Sparse attributes and adjective-heavy descriptions are the main reason a catalogue is skipped — the fix is unglamorous data work, not a schema trick.
The situation
A concrete version of this.
A retailer with a substantial catalogue where product pages were written for human persuasion and populated with minimal structured data.
- Descriptions lead with adjectives rather than specifications
- Attribute fields are sparse or inconsistently populated
- Variants are modelled inconsistently across categories
- The brand appears in search results but not in AI recommendations
Why it matters
When an assistant assembles a recommendation it needs to match attributes against a stated need. A catalogue that cannot answer 'is this suitable for X' in structured form is not a candidate, regardless of how well it ranks.
What gets built
Three things, in this order.
- 01
Attribute completeness as a measured programme
Establish which attributes matter per category, audit coverage, and close the gaps — prioritised by revenue rather than alphabetically.
- 02
Structured data that is consistent and correct
Product markup applied uniformly, with variants, availability and pricing modelled the same way across the catalogue.
- 03
Retriever access verified
Confirm the crawlers feeding AI answers can actually reach product and category pages. Accidental blocking is more common than anyone expects.
The metric
Attribute completeness by revenue-weighted category, alongside assistant referral traffic
Referral traffic is the outcome but it moves slowly and is partly outside your control. Attribute completeness is the input you can actually manage week to week.
The boundary
What stays with a person
No fabricated specifications to fill gaps. An attribute that is not known is left empty — a wrong specification is worse for both customers and credibility than a missing one.
Related
More for E-commerce & Retail.
Working example
AI Growth Console
A working system for this sector. Open the console and follow one record all the way through it.
Open AI Growth ConsoleYou already know which process is bleeding.
Recognise the scenario?
Tell us how it actually runs in your business today, including the workarounds. Or email contact@koderead.com.
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