AI Visibility Score: The 68,000 AI Response Method for E-commerce

One client, 1,400 product pages, zero AI answer visibility

A client calls me on a Tuesday morning. He sells small kitchen appliances. 1,400 product pages. 4,300 organic sessions a month. And the phrase that keeps coming up: « ChatGPT always cites the same competitor. »

We look. I run through 47 conversational queries. The questions a buyer asks ChatGPT, Perplexity, or Gemini before clicking. « What is the best quiet bean-to-cup coffee machine? » « How do I choose a juicer that’s easy to clean? » « Which stand mixer for kneading bread dough? »

Result. One mention. Position 9. Across 47 queries. Score: 4 out of 100.

The content was solid. The architecture was not.

Neil Patel just published a study on 68,000 AI responses in fintech. Same observation. A brand doesn’t win because it has a good website. It wins because it appears often, early, and across several question catégories. Three variables. One 100-point score.

I’ve applied this score to e-commerce for several months now. Not just to neobanks. To product pages.

The first thing I say to this client? We’re not going to produce 200 useless texts. We’re going to measure. 47 queries. 3 dimensions. A starting point.

Why 47? Because it’s a manageable sample. Not 500. Enough to see patterns. Enough to keep a weekly tracking rhythm.

I often say: a score of 4 scares you. A score that moves to 9 motivates. Progression becomes visible. That’s the first lever of the DOSE framework. Dopamine.

Before running, you need to understand the exact mechanics. How many times? In what position? In how many catégories?

And above all, why 68,000 fintech responses change the way you manage a 1,400-SKU catalog.

Neil Patel doesn’t say you need to be everywhere. He says you need to appear in the AI-generated answers for high-intent questions. In fintech, the best brands are cited early, often, across several types of queries. In e-commerce, same logic.

A buyer doesn’t want to know who has the biggest SEO budget. He wants a clear answer. If the AI cites your category page for a choice question, you gain a position that Google doesn’t even show on page one anymore.

That’s why I advise starting now. Each month with measurement, you protect conversational positions nobody sees. The score tells the truth.

So we started with this client. 47 queries. A score of 4. And one simple question: what happens if we push frequency to 15, average position to 3, and coverage to 4 catégories?

Three levers decide what comes next.

Le score ne tombe pas du ciel : il agrège trois mesures prises sur vos 47 requêtes cibles. Voici le pipeline, du panier de questions jusqu’au score final.

The AI Visibility Score is three measures. Not one.

Neil Patel’s score combines three signals. How often a brand appears. In what position. In how many question catégories.

I kept that logic for e-commerce. It comes down to three words: frequency, position, coverage.

The first variable: frequency. Across your 47 queries, how many times is your brand cited? Not your site. Your brand. If ChatGPT says « the best bean-to-cup coffee machines are X, Y, Z », and you are Y, you count. Zero mentions? Zero points.

The second variable: position. Being cited first is worth more than third. In an AI answer, the first two brands capture attention. The third exists. The fifth is background noise.

The third variable: coverage. Does your brand appear in one question category? Or in budget, comparison, usage, maintenance questions? The wider the coverage, the higher the score.

I calculate a 100-point score from these three variables. Frequency is worth 50 points. Position is worth 30 points. Coverage is worth 20 points. That’s my e-commerce weighting. It’s not set in stone. It provides a framework.

Why this weighting? Appearing often is the first lever. Then ranking well. Then expanding catégories. A brand that appears once, in first position, in one category, is hanging by a thread. A brand that appears 15 times, in position 2, across 4 catégories, is established.

The score only matters if it’s tracked over time. An isolated number says nothing. A series of scores says everything.

Example. Score of 4 at the start. Six weeks later, score of 27. The gain is there. You see it. You can explain it in a meeting without a 40-slide PowerPoint.

In the method I learned from Guillaume Attias at BMO Academy, this kind of progression is not a detail. It’s the fuel of execution. Dopamine.

We’ll get back to the DOSE framework below. First, the concrete application to an e-commerce catalog.

Here’s how I calculate a score:

• Frequency: (number of mentions / number of queries) × 50. Example: 15 mentions out of 47 → 15.95 points.
• Position: score out of 30 based on average position. First place → 30. Second → 22. Third → 15. Beyond → 5.
• Coverage: (number of catégories covered / 6) × 20. Four catégories → 13.33 points.

The total reads out of 100. No secret formula. Just an indicator.

With my clients, I calculate this score by hand in a spreadsheet. No paid tool needed. A list of queries. One column per AI. One column for the mention. One column for the position. One column for the category.

One hour a week is enough to track 47 queries. Less time than writing a blog post nobody will ever read.

Measurement precision matters. An approximate score hides the real levers. Stagnant frequency, slipping position, plateaued coverage: each signal calls for a different action.

68,000 fintech responses aren’t enough. Here’s the e-commerce adaptation.

Neil Patel’s study focuses on fintech. Questions like « What’s the best budget tracking app? » or « How do I send money to friends and family? ». The cited brands are apps. The catégories are financial use cases.

An e-commerce site doesn’t have the same queries. It sells products. Buyers ask selection, compatibility, budget, usage, and maintenance questions.

Real example observed with a small kitchen appliance client. Three query families dominate:

• Selection questions: « Which stand mixer for kneading bread dough? »
• Comparison questions: « Bean-to-cup coffee machine or espresso machine with grinder: which to choose? »
• Usage questions: « How do I maintain my bean-to-cup coffee machine daily? »

Each family maps to an AI Visibility Score category. A brand that appears only in selection questions has limited coverage. It remains fragile if a competitor covers all three.

Reusing the 68,000 responses as-is makes no sense. I redo the exercise on a sample of catalog-specific queries. 47 queries. 10 to 12 catégories. Three conversational engines: ChatGPT, Perplexity, Gemini.

Why three engines? Because they don’t cite the same sources. ChatGPT may cite a guide page. Perplexity may cite a customer review. Gemini may cite a product page. A brand present on only one engine gets a partial score.

I observe among my clients that multi-engine coverage is often the weak point. Lots of effort on classic Google. None on conversational queries. The score shows it immediately.

The table I use:

This table diagnoses; it doesn’t decorate. It shows two things: where you’re cited, and where you could be.

Then comes action. A low coverage score means missing pages. A low position score means an architecture problem. A low frequency score means a lack of legitimacy.

That’s what we fixed for the client with 1,400 product pages.

In fintech, winning brands answer use cases. In e-commerce, they answer buying intents. The mechanics remain the same. The vocabulary changes, and so do the actions.

I always start with query mapping, never with a content audit. Conversational query first, page second, score to verify.

Dopamine: a rising score changes execution

The AI Visibility Score has a side effect. It makes progression visible. And that visibility changes the game.

In the DOSE framework I learned from Guillaume Attias at BMO Academy, dopamine is the first lever. Motivation doesn’t come from slogans. It comes from a moving measure.

A score of 4 motivates no one. A score that moves from 4 to 9, then 9 to 15, creates a loop. Every week, the client wants to see the line go up. Tracking becomes a ritual. Not a chore.

I saw this effect with an e-commerce client. At first, he told me: « AI brings me nothing, it’s a gadget. » Six weeks later, he was asking me to send the spreadsheet every Monday. Why? Because he saw cells go from « No » to « Yes ».

That’s the principle of dopamine. A small measurable win. Not a six-month traffic promise. An immediate, observable, repeatable win.

I formalize it like this:

• A list of 47 queries, frozen for six weeks.
• A weekly score calculation, by hand, in a spreadsheet.
• An intermediate goal of 9, then 15, then 27.
• A 15-minute touchpoint on Monday morning. No 20-page report.

This ritual is simple. One person can run it. That’s what I want: a system that runs without me.

The AI Visibility Score acts as the backbone. It links the conversational query to the existing page. It makes visible what Google Analytics doesn’t show.

Most SEO dashboards contain no conversational data. Sessions. Positions. Clicks. And not a single AI mention. The score fills that gap.

And when you show it to an e-commerce director, the reaction is immediate. The number replaces abstraction.

A number that goes up.

What remains is to know which actions make that number go up. That’s the subject of the next sections.

The concrete case first. The method after.

Dopamine works only on one condition: that the measure is tracked for real. Not once a quarter. Once a week. Otherwise, the signal weakens. The loop fades. The score becomes just another number in a report.

Le meilleur argument reste un cas réel. Voici comment le score d’un client est passé de 4 à 27 sur 100, étape par étape.

Client case: 47 queries, 6 weeks, score from 4 to 27

The client with 1,400 product pages started far behind. Score of 4 out of 100. One mention across 47 queries, in position 9, in one category.

We didn’t rebuild the site. We didn’t produce 100 articles. We did three things.

First, restructure the category pages. Each page received a clear answer to the selection question. Not a 2,000-word wall of text. A 180- to 250-word response, placed above the filters. « Here’s what to look for when choosing a quiet bean-to-cup coffee machine. »

Then, create a comparison guide per product family. A guide attached to the catalog, linked to product pages, not a blog post. It answers the comparison question: « Bean-to-cup coffee machine or espresso with grinder: which to choose? »

Finally, enrich usage questions on product pages. Each page received a « Maintenance and usage » block of 80 to 120 words. Direct answers to questions asked on ChatGPT and Perplexity, not marketing content.

Result after six weeks, observed with this client:

• Frequency: 15 mentions out of 47 queries, compared to 1 at the start.
• Average position: 2.8, compared to 9.
• Coverage: 4 catégories out of 6, compared to 1.
• Score: 27 out of 100.

Classic organic traffic moved too. 4,300 sessions a month at the start. 5,100 six weeks later. Not an explosion. A signal.

The absolute number matters less than the trend. The score multiplied by 6.75 in six weeks. No advertising. No massive content production.

What made the difference? Architecture. The pages already existed. They just didn’t answer conversational questions. We adjusted existing content, added linked guides, and clarified intent.

I’m not saying this result repeats everywhere. Every catalog is different. But the method is replicable. Measure, then act on architecture, then measure again.

The client now has a score of 27. He’s aiming for 45. The first win is there: he knows what to track.

And you? How many of your 47 high-intent queries trigger a mention of your brand today?

In this type of mission, I don’t sell a traffic promise. I show pages. The score is just the measured translation of pages that finally answer the right questions.

What I fix first: the semantic architecture of the catalog

The score is only a reflection. The action happens upstream, in how the catalog is organized.

At most e-commerce companies, product pages are isolated. Category pages are result lists. Buying guides are blog posts disconnected from the catalog. Result: AIs can’t find a reliable source and prefer to cite a competitor.

The fix starts with a map of 47 conversational queries. Three columns: intent, existing page, missing page.

Example. The query « Which stand mixer for kneading bread dough? » maps to a selection intent. The existing page is the « Stand mixers » category. But it doesn’t answer the question. It lists 32 products. A written, visible answer is missing.

We fix it by adding a 180-word block under the H1 title. No fluff. A direct answer: « To knead bread dough, first look at wattage, motor type, and bowl capacity. Direct-drive models are the quietest. »

Then we link this page to comparison guides and product pages. A star-shaped internal linking. Each conversational query points to a resource page. Each resource page points to relevant products.

That’s what I call a semantic cocoon. Concretely, it’s an architecture.

Here’s the order I follow with my clients:

• List 47 high-intent conversational queries.
• Classify into 6 catégories: selection, comparison, budget, usage, maintenance, compatibility.
• Audit each existing page: does it answer the question? In how many words? In what position?
• Prioritize fixing category pages and guides, not product pages.
• Create missing pages when the intent justifies it.

The rule I set: one page, one question, one answer. Not three questions per page. No diluted content.

Neil Patel’s study points the same way: winning brands are those whose pages are cited as sources. And a source is a specific page that answers precisely, not an entire site.

In e-commerce, that precision comes from architecture. Word volume is not enough.

A client once told me: « We have 800 product pages, we should be visible everywhere. » No. Conversational visibility isn’t declared. It’s structured.

And when the structure is in place, the score climbs almost on its own.

Why not start with product pages? Because a product page answers an already specific need. The conversational question is about choice. The category page and comparison guide carry more weight. They capture the AI mention. The product page then converts. Order matters.

Your SEO dashboard must change. Here’s what it looks like.

I’m not replacing classic metrics. I’m adding a conversational column.

An e-commerce dashboard in 2026 benefits from showing four blocks:

• Classic organic sessions, month by month.
• Google positions on the 47 target queries.
• The AI Visibility Score out of 100, calculated every Monday.
• Variations across three dimensions: frequency, position, coverage.

Three dimensions. Not fourteen. I’m not trying to drown the reader. I want to focus.

The 100-point score serves as a result indicator. The three dimensions serve as lever indicators. Score stagnating? I look at the dimension that’s dropping. Frequency plateauing? I reinforce architecture. Average position slipping? I refine answers on existing pages.

This steering takes one hour a week and a spreadsheet. No magic tool.

With my clients, this tracking plugs into existing routines. No dedicated meeting. A 15-minute check-in on Monday, and the loop runs.

The system turns a vague decision into a measurable sequence. No more « does the AI like us? » We look at the number, act, and measure again.

The DOSE framework is for execution, not coaching. Dopamine rises when the score rises. With my clients, that’s often the moment they stop enduring and start steering.

Last thing. The AI Visibility Score doesn’t exist as a standard. It’s an adaptation of the Neil Patel study. The need is there: AI answers display before classic results. Whoever measures first gains the advantage.

You can wait for Google to impose a metric. Or start next Friday with 47 queries and an initial score.

The question I always ask my clients, I ask you: across the 12 queries that trigger a purchase in your catalog, how many times is your brand cited today?

If the answer is « I don’t know », you know where to start.

And if it’s « never », the field is open. Excellent news. You don’t have to dethrone a competitor. You just need to structure the right pages before they do.

Stéphane Jambu

SEO & AI Engineer

I build growth systems / AI / Neuroscience | 650+ clients · 80 LinkedIn testimonials · 30 years of expertise · 15 years of systems running without me.

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