Attribute completeness audit
Which items are missing the attributes agents require, quantified by revenue exposure rather than by row count, so the fix order follows the money.
What a shopping agent actually reads
Agents read structured product data rather than page layouts. They need complete attributes — identifiers, brand, condition, stock state, cost fields and shipping terms — to compare items reliably. A catalogue with sparse or inconsistent attributes cannot be compared, so it gets excluded from the shortlist entirely.
The shift here is from category to item. Classic e-commerce SEO competes for a category page to rank for a broad term, then relies on the shopper browsing. An agent skips that entirely: it is asked for a specific thing under specific constraints, and it returns individual products that satisfy them.
That makes attribute completeness a commercial issue rather than a technical hygiene one. If a buyer asks for waterproof boots in a particular size under a stated budget, an agent can only consider items where size, material and cost fields are all populated and machine-readable. A product missing one of those is not ranked lower — it is simply not a candidate.
Accuracy matters as much as completeness, and differently from classic SEO. A stale stock state on a ranking page costs you one frustrated visitor. A stale stock state in a feed an agent trusts costs you a recommendation and, if the agent notices repeatedly, degrades how much it trusts your catalogue in general.
There is also a specificity problem most catalogues share. Descriptions written for browsing shoppers use evocative language that tells an agent nothing checkable. Agents match against attributes and concrete claims, so a description that says a bag is 'perfect for weekend adventures' is useless where one stating capacity in litres is not.
What the work covers
The work covers a completeness audit across your catalogue, the attribute gaps that exclude items from agent consideration, accuracy monitoring on stock and cost fields, structured data aligned with your feed, and measurement of which products agents actually surface when asked.
Attribute enrichment
The gaps closed — identifiers, materials, dimensions, compatibility, condition — sourced from your systems or supplier data rather than invented.
Accuracy monitoring
Stock states and cost fields checked against your live store daily, because a feed an agent stops trusting is worse than a feed it has never seen.
Agent-legible descriptions
Product copy rewritten to carry checkable specifics — capacity, dimensions, compatibility — while remaining readable for the humans who still browse.
Structured data alignment
On-page Product markup reconciled with your feed, so an agent reading either source gets the same answer instead of two conflicting ones.
Agent visibility panel
A fixed set of shopping prompts run weekly, recording which of your products get surfaced and which competitor items appear instead.
How the work runs
The work starts with a fixed-scope audit establishing which of your items agents can currently consider, then moves into ongoing management: closing the attribute gaps, monitoring accuracy daily, and re-running a shopping prompt panel weekly to see which products actually get surfaced.
- 01
Audit what agents can see
Every item scored for attribute completeness against what agents require, weighted by the revenue behind it. Most catalogues have a long tail that is structurally invisible.
→ A completeness score and a revenue-weighted gap list
- 02
Close the attribute gaps
Missing attributes populated from your product systems, supplier data or specifications. Where a value genuinely cannot be sourced, we say so rather than guessing at it.
→ An enriched, agent-complete feed
- 03
Establish accuracy monitoring
Daily reconciliation between feed and live store on stock and cost fields, with alerting when they diverge, because trust in a catalogue is lost gradually and regained slowly.
→ Monitoring and divergence alerting live
- 04
Measure agent surfacing
A fixed panel of shopping prompts run weekly against the assistants your buyers use, recording which of your items appear and which competitors displace them.
→ Weekly agent surfacing report
Classic e-commerce SEO compared with agent optimisation
They compete for different units. Classic e-commerce SEO works to rank a category page and relies on the shopper browsing from there. Agent optimisation works to make an individual item a valid candidate for a specific request, where an incomplete record is not a candidate at all.
| Classic e-commerce SEO | Agent optimisation | |
|---|---|---|
| Unit that wins | The category page | The individual item |
| What is read | Page content and links | Structured attributes in a feed |
| Missing detail | Ranks slightly lower | Excluded from consideration entirely |
| Description style | Evocative, written for browsing | Checkable specifics an agent can match on |
| Stale stock state | One frustrated visitor | Lost recommendation and reduced catalogue trust |
| Update cadence | Weekly is usually fine | Daily, reconciled against the live store |
Signals you need this now
You need this when assistants recommend competitor products for requests that your own catalogue satisfies, when a large share of your items lack the attributes agents require, or when your feed and your product pages disagree about stock and cost fields.
- Assistants recommend competitors for requests your catalogue satisfies
- A large share of items lack identifiers, dimensions or condition data
- Your feed and product pages disagree about stock or cost fields
- Product descriptions are evocative but contain no checkable specifics
- Category pages rank well while individual items are never surfaced
- Nobody monitors feed accuracy between scheduled exports
- You sell into categories where buyers compare on specifications
What clients see
Attribute completeness produces the fastest change, because an item that was structurally ineligible becomes a candidate as soon as its record is complete. Accuracy work compounds more slowly, protecting the catalogue-level trust that determines whether agents draw on you at all.
Where this fits
Related GEO services
Common in these sectors
The adjacent disciplines
Deciding between options
Questions about shopping agent optimization
Each answer is written to stand alone in 40 to 60 words — the shape an AI Overview or Perplexity citation lifts. Ships with FAQPage schema.
How is this different from standard shopping feed management?
Standard feed management optimises for advertising platforms, which tolerate sparse records and rank them lower. Shopping agents exclude incomplete items from consideration entirely, so completeness becomes a threshold rather than a ranking factor, and the attribute set that matters is broader.
Which attributes matter most to agents?
Identifiers, brand, condition and stock state are the baseline. Beyond that it is category-specific: dimensions and materials for physical goods, compatibility for components, capacity for containers. The rule is that anything a buyer might constrain on needs to be a structured field.
Do we need to rewrite every product description?
No. We prioritise by revenue exposure, so the items carrying your sales get rewritten first and the long tail follows. Enrichment of structured attributes usually matters more than description rewriting, and it can often be automated from existing product data.
How often does the feed need updating?
Daily reconciliation against your live store for stock states and cost fields. Attribute enrichment is less time-sensitive. The asymmetry matters because an agent that repeatedly finds your data wrong reduces how much it draws on your catalogue overall.
Does this help with Google Shopping too?
Generally yes. The completeness and accuracy work benefits any platform consuming your feed, and clients frequently see conventional shopping performance improve alongside agent visibility. The agent-specific work is the attribute breadth and the description specificity.
How do you measure whether agents are surfacing our products?
A fixed panel of shopping prompts run weekly against the assistants your buyers use, recording which of your items appear, in what position, and which competitor products displace them. Same methodology as the rest of the pillar, applied at item level.
Find out whether this is your constraint.
Thirty minutes with a senior strategist. We pull your live visibility while we talk and tell you plainly whether a shopping agent optimization is what you need — or whether your problem sits somewhere else.