Traditional Amazon competitive analysis looks at price, ratings and Best Seller Rank. Semantic competitive analysis looks at how Rufus and Amazon's AI actually compare your products. Here's how to do it.
Competitive analysis has been a cornerstone of Amazon strategy for years. Pricing. Ratings. Review counts. Best Seller Rank. Useful, but surface-level. They tell you what's happening on the shelf - they don't tell you what [Amazon's AI](/blog/what-is-amazon-rufus-2026) thinks of you.
Semantic competitive analysis is the layer underneath. Rather than tracking visible performance metrics, it examines how competitors structure their content semantically, establish entity relationships and position themselves for AI understanding.
It's the difference between knowing your competitor sells more and knowing why Rufus picks them when a shopper asks "best [category] for [use case]".
> **Update — May 2026:** Amazon has merged Rufus with Alexa+ to create **Alexa for Shopping**, now live on the Amazon Shopping app, website and Echo Show. References to "Amazon Rufus" in this post relate to the predecessor product. [Read Amazon's announcement.](https://www.aboutamazon.com/news/retail/alexa-for-shopping-ai-assistant)
## How LLMs actually compare and differentiate products
To do this work, you need a clear picture of what the model is doing under the hood.
**Entity-based comparison.** LLMs don't match keywords. They build entity-based understanding of each product's attributes and characteristics. When a shopper asks about differences between brands, the model identifies the specific entities being compared, extracts key attributes for each, determines which attributes are comparable and surfaces meaningful differences. If your attributes are vague or scattered, the model can't include you in the comparison.
**Confidence-weighted responses.** When generating comparative answers, models assign confidence scores to information. Information that appears consistently across multiple sources gets higher confidence than isolated claims. This is why D2C–Amazon consistency matters: contradictions reduce confidence and low-confidence claims get dropped from the answer.
**Contextual relevance filtering.** Models don't surface every difference between two products. They filter by what's relevant to the query. A page that lists 30 features doesn't help if the model can't tell which three matter for the asked question.
**Narrative construction.** Advanced models construct comparative narratives that help shoppers decide. "Brand A is lighter and better for long runs, Brand B is more durable and better for trail." That narrative is built from extracted attributes - your job is to make sure your attributes get pulled in.
## Conducting a semantic gap analysis
The foundation of the work is a structured gap analysis comparing how your products and competitors are represented semantically.
**Entity coverage assessment.** Identify all entity types relevant to your category. Document entity coverage across competitor listings. Score how comprehensively each competitor establishes the entities a shopper would care about. Identify the gaps in your own listings.
For example, in the running socks category, the relevant entities include fit (low cut, no-show, crew, knee-high), construction (cushioning, blister protection, seamless toe), use case (marathon, trail, recovery, daily training) and constraint (sweat-wicking, moisture-control, anti-microbial). A competitor that covers all four entity dimensions in their bullets and A+ wins inclusion. A brand that only covers two doesn't.
**Attribute specificity comparison.** Compare the specificity of measurements and specifications. Quantitative beats qualitative. "12 hours of cushioning under 50km loads" beats "long-lasting comfort." Models prefer numbers and conditions.
**Relationship mapping.** Examine how competitors establish relationships between entities. Compatibility relationships ("works with [system]"), problem-solution relationships ("prevents [issue] when [condition]") and comparative relationships ("vs [alternative], offers [difference]"). Each one is a citation hook.
**Semantic structure comparison.** Compare content organisation, heading hierarchy and information chunking. Models extract from well-structured content faster than from prose blocks. A clean H2/H3 hierarchy with entity-rich headings beats a wall of paragraphs every time.
## AI response pattern analysis
The other half of the work is testing how AI systems currently represent your competitors.
**Query response mapping.** Develop a query set covering category, feature, problem and competitive dimensions. Test each query across Rufus, ChatGPT, Gemini, Perplexity at regular intervals. Record which competitors appear and how they're positioned. Look for response patterns - which brand is consistently named first? Which is positioned as "premium"? Which gets the "budget" label?
**Feature emphasis analysis.** Document which product features each AI system highlights for each competitor. Compare emphasis patterns across query types. The features that get repeatedly highlighted are the ones the model has high confidence in - and they're the features your content needs to cover with equal clarity.
**Competitive positioning assessment.** Evaluate how AI systems position competitors relative to each other. Premium, budget, mid-range. Use case specialisations. Competitive advantages emphasised. This is the qualitative read - and it tells you the story the model is telling, which is the story you need to either reinforce or counter.
## Why Lexem.io matters here
This is where our [Lexem.io](/blog/inside-lexemio-how-clustering-buying-intent-2025) tooling does the heavy lifting.
Lexem.io clusters real buyer prompts by intent and stage. When you run your category through it, you see the full prompt set you should be testing against - not just your top 20 keywords, but the genuine question variants shoppers use. The clusters surface where you and your competitors share territory and where the "semantic collisions" happen. (We've covered the [semantic collision concept](/blog/semantic-collision-what-why-why-happens-2025) in more depth.)
The output goes straight into the response-pattern test. You run the prompts. You log who gets named. You map the feature emphasis. And you have a concrete picture of where your semantic position is weak - and exactly which content to fix to close the gap.
## What to do with the findings
Three concrete moves come out of any semantic competitive analysis.
**Close the entity gaps.** Where your bullets and A+ don't cover an entity dimension your competitors cover, write the missing content. Don't add it as filler - add it as quotable, attribute-rich answer material.
**Match attribute specificity.** Where competitors use numbers and you use adjectives, swap. "Premium materials" loses to "Italian merino, 200gsm, mulesing-free."
**Reinforce or counter the model's narrative.** If the model has decided you're the "budget" option and you want to be the "performance" option, you have to flood your content with the performance entities - review echoes, A+ proof, image overlays - until the narrative shifts. This takes weeks of consistent updates, not one rewrite.
## Bottom line
Semantic competitive analysis is what separates brands that "do Amazon SEO" from brands that engineer for Rufus. The visible metrics tell you what's happening. The semantic layer tells you why - and gives you the levers to change it.
If you want to see your category through this lens, [Lmo7 runs semantic competitive audits](/contact) using Lexem.io and our prompt-testing framework. We typically find the three highest-leverage content moves in the first week.
That's the shift.