Rufus Score
Rufus Score grades Amazon SKUs on their fit with Rufus and the COSMO knowledge graph. It evaluates 15 relations including A+ content depth, structured bullet coverage, brand-store consistency, Q&A density, and review keyword variance. Each relation contributes to a 0-100 composite plus a ranked recommendation queue.
What Rufus is
Rufus is Amazon's AI shopping assistant. It answers shopper questions by retrieving from the COSMO knowledge graph — a structured representation of products and their relations that Amazon has documented in research papers. The signals Rufus weights are different from a generic product graph: it cares about A+ content, structured bullets, brand-store consistency, Q&A density, and the relations between SKUs (substitutes, complements, audiences, use cases).
This is why eCommerce Insights scores Amazon SKUs through a separate model. See channel-aware scoring for the routing logic.
The 15 COSMO relations
Each relation maps to a question Rufus might ask of a product node in the graph.
| # | Relation | What it captures |
|---|---|---|
| 1 | A+ content depth | Brand-story modules, comparison charts, image-with-text blocks. |
| 2 | Structured bullets | Bullets quote concrete specs in a consistent format. |
| 3 | Brand-store consistency | SKU is listed in the brand store with consistent imagery and copy. |
| 4 | Q&A coverage | At least eight shopper questions answered on the listing. |
| 5 | Review keyword variance | Reviews cover multiple use cases, not a single repeated frame. |
| 6 | Substitute relation | Listing surfaces credible substitutes (frequently-bought-with patterns). |
| 7 | Complementary relation | Listing surfaces complements (bundles, related accessories). |
| 8 | Use-case relation | Listing maps to one or more named use cases. |
| 9 | Audience relation | Listing maps to one or more named audience segments. |
| 10 | Attribute relation | Structured attributes (size, colour, material) are filled. |
| 11 | Category relation | Listing lives in the right node category, not a generic fallback. |
| 12 | Brand-tier relation | Brand is positioned at a consistent tier (premium / mid / value). |
| 13 | Price-tier relation | Price sits inside the expected band for the category. |
| 14 | Seasonality relation | Listing carries seasonal signals where appropriate (holiday gifts, summer use). |
| 15 | Compatibility relation | Listing names compatible products / platforms / standards. |
Running an audit
Add an Amazon URL
Catalogue import recognises
amazon.comand equivalents and routes the SKU to Rufus Score automatically.First fetch
eCommerce Insights reads the listing using its standard fetcher with Amazon-aware parsing. ScrapingBee fallback if the listing returns a bot challenge.
Relation extraction
The 15 relations are extracted from the fetched HTML, the A+ content, the brand-store reference (if linked), and the Q&A block.
Score
Each relation is scored 0-100 and combined into the composite. The composite weights favour the relations that most predict Rufus citation in our internal Prompt Runs.
Reading the result
The header shows the composite plus the delta since last run. Below, the fifteen relations are stacked. Each row shows the relation name, the sub-score, the observation (what eCommerce Insights actually saw on the listing), and a one-sentence recommendation.
Recommendations
Recommendations translate to four kinds of work in Seller Central.
- A+ content edits — block-level rewrites. Linked through to a Listing rewrite capsule with proposed copy.
- Bullet rewrites — five bullets, structured pattern.
- Brand-store updates — module-level copy. Useful when several SKUs share the same brand-store hub.
- Q&A submissions — proposed questions and answers the brand can submit to fill gaps.
Limits and unknowns
- eCommerce Insights is read-only against Seller Central as of v19. Recommendations are applied manually.
- The COSMO relation set is what Amazon publicly documents. The model itself may weight other signals; we revalidate the set quarterly.
- Sponsored placements are not modelled here — Rufus Score is organic-citation-focused.
Common questions
Why does Amazon need its own score?
Where do the 15 relations come from?
Can I push Rufus recommendations back to Amazon Seller Central?
Does Rufus Score apply to Walmart?
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