Win the product questions that shape purchase decisions.
Measure which products and competitors AI recommends, then improve the product facts, comparison evidence and third-party signals behind those answers.
Focus the GEO program on decisions that matter.
Measure which products and competitors AI recommends, then improve the product facts, comparison evidence and third-party signals behind those answers.
Product discovery
Track category, use-case and “best for” questions before they become invisible demand.
Comparison evidence
See which attributes, reviews and independent sources support the brands AI recommends.
Catalog consistency
Align product details, structured data and third-party descriptions across the discovery journey.
A practical path from signal to action.
Keep measurement comparable while giving each finding supporting evidence and a next check.
What decides AI visibility for e-commerce & dtc
Product categories behave differently from every other segment in AI answers: they consolidate. In our own Q3 2026 benchmark, the three categories with a clear default answer were all product categories — project management software at 38% of answers, skincare at 35%, electric bikes at 28% — while every local-service category we measured had a leader below 13%.
| You are displacing an incumbent, not filling a void | When one brand already appears in a quarter or more of answers, being merely present is not enough. The work is to become the answer to a specific sub-question the incumbent answers badly — a constraint, a budget, a use case — rather than competing on the head term where it is entrenched. |
|---|---|
| Retailers and marketplaces are the competition for citations | In the product categories we measured, a large share of citations went to marketplaces, review sites and publisher round-ups rather than to brand sites. Your product page can be perfect and still lose the citation to a listing page that carries the same facts with third-party framing. |
| Variants break product structured data quietly | On Shopify, a product with multiple variants emits ProductGroup rather than Product, and offers take a different shape. Audits that assert "@type must be Product" will report a false failure across your whole catalogue, which is why catalogue-wide schema checks need to understand variants before they are trusted. |
| Facts, not adjectives, are what gets quoted | Assistants lift sentences that answer a constraint: a dimension, a material, a return window, a compatibility. Category copy written for brand tone gives them nothing to lift, however good it reads. |
Start with these three
- Find the questions in your category where the assistants currently name nobody — those are open ground, and cheaper than displacing an incumbent.
- Check whether the marketplaces and review sites that get cited in your category carry your current facts, prices and availability.
- Put dimensions, materials, compatibility and returns into sentences on the product page, not only into a spec table.
See the question, answer status and evidence together.
Sample UI demonstrates the evidence depth of a formal project.
Visibility evidence
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Questions teams ask before they start
Does a snapshot replace onboarding?
No. A snapshot is a limited sample. A formal project still needs confirmed markets, competitors and monitored prompts before continuous measurement begins.
Will snapshot data be charged twice in the App?
Not by default. Claimable snapshot results can be inherited, while a new formal run starts only after onboarding is confirmed.
Can teams measure more than one market?
Yes. Markets and languages should be treated as separate measurement segments so results remain comparable.
How long before results are meaningful?
A single run gives a baseline. Trends need several runs, because one sample cannot separate a real change from model variance.
What is actually needed to get started?
One domain. The brand profile, traceable facts and buyer questions are generated from it and stay editable before any formal run.
Who on the team uses this day to day?
Usually whoever owns content and organic growth. The measurement side needs no engineering work, and publishing always stops at a human approval step.
Turn AI visibility into a repeatable growth system.
Create a project, confirm your market and start measuring the questions that matter.