About this worked example
The shop on this page is made up. We do not publish customer names, customer numbers, or customer quotes.
The scoring is real. Every metric name and every weight below comes straight from the GEO-Score engine that runs your analysis.
So the shop is imaginary. The maths is not. Run the same analysis on your own product page and you get real numbers back.
Why product pages are hard for AI search
Descriptions are too short
Most product pages carry a spec table and two lines of marketing copy. An AI assistant has almost nothing to summarise. It picks a competitor page that explains more.
Every page looks the same
Shops often reuse the manufacturer text on hundreds of pages. AI engines see near-identical pages and cannot tell which one deserves the citation.
Buyer questions go unanswered
People ask AI assistants practical questions. Will it fit a small room? How do I clean it? A page that answers those questions gets recommended. A page that only lists dimensions does not.
The crawler never arrives
Many shop platforms ship a default robots.txt that blocks unknown user agents. If GPTBot or PerplexityBot cannot fetch the page, nothing else on this list matters.
The starting point
Picture an online shop selling home furniture. It has around 500 product pages, a handful of category pages, and no blog.
Traditional search works fine. The shop ranks for its product names. But when someone asks an AI assistant for a sofa recommendation, the shop is never mentioned.
The owner runs a GEO analysis on one representative product page. The overall score comes back low, and the report shows which of the 22 metrics pulled it down.
That last part is the useful bit. A single overall number tells you little. The per-metric breakdown tells you exactly where the missing points are, and how many of them there are.