What Is the AI Shopping Customer Journey?

A merchant on r/ecommerce put it plainly: "Page 1 for every product category. ChatGPT recommends our competitors in every single one."

That post got a lot of upvotes — because it describes something a lot of ecommerce brands are quietly experiencing. Strong SEO. Solid rankings on Google. Products people actually buy. And yet, when a customer asks ChatGPT, Perplexity, or Google AI Mode to recommend a dress, a skincare product, or a pair of running shoes, a competitor appears and they don't.

The instinct is to blame product data or schema markup. Sometimes that's the culprit. But there's a deeper structural problem most brands haven't considered: they're only trying to appear at one or two stages of a nine-stage customer journey.

AI search doesn't work like a keyword match. It responds to intent — what the customer is actually trying to accomplish at each moment of their journey. And that journey, from first recognising a problem to seeking help after a purchase, has nine distinct stages. Each one is a different type of query. Each one produces different AI behaviour. And brands that only optimise for the obvious stages — "best deal" searches, "where to buy" searches — are invisible for the other seven.

We know this because we tested it. We ran 8,520 real shopping queries through ChatGPT across all nine intent stages and tracked every product carousel, every brand mention, and every citation. What those 8,520 queries revealed about AI shopping behaviour would surprise most ecommerce marketers. This post goes deeper — into the journey itself, and what it means for your brand's visibility strategy.

What is the ecommerce customer journey in AI search?

In traditional SEO, the customer journey was loosely mapped to query types: informational, navigational, transactional. You created content for each and hoped to intercept the right searcher at the right moment.

In AI search, the journey is more granular and more consequential. When a customer asks ChatGPT a question, the AI isn't just returning results — it's synthesising an answer based on what it knows about the query's intent. A customer asking "what should I look for in a dress that doesn't wrinkle?" gets a completely different type of response than a customer asking "best midi dress under $100." Different format, different brands mentioned, different commercial signals triggered.

The implication: your brand's AI visibility isn't a single number. It's nine different numbers — one for each intent stage — and most brands have never measured them.

The 9 Shopping Intent Stages in AI Search

Based on our research across 8,520 ChatGPT queries, every ecommerce shopping query falls into one of nine behavioural intent stages. These aren't theoretical — they're derived from how real customers actually phrase questions to AI assistants.

Stage 1: Problem Recognition

The customer hasn't decided to buy anything yet. They have a problem, a frustration, or a need they're trying to articulate.

"My dress keeps wrinkling after washing. What should I look for instead?"
"I never feel comfortable in what I wear to work. Where do I start?"

This is the earliest possible point of influence — before the customer even knows what category they're shopping in. Brands that appear here shape the entire journey that follows.

AI behaviour: Product carousels are triggered only 39.8% of the time at this stage. The AI mostly responds with educational content — fabric guides, fit advice, occasion tips. Brands mentioned in that content become part of the customer's mental shortlist before any comparison has begun.

Stage 2: Category Exploration

The customer knows what they're looking for broadly but hasn't applied any constraints.

"What are some reliable dress options for everyday wear?"
"What types of summer dresses are most popular right now?"

This is discovery mode. The customer is open to any brand, any price point, any style. Carousel trigger rate climbs to 72.2% — more than double the Problem Recognition rate.

AI behaviour: Brands with broad catalogue coverage and well-structured product data appear most frequently here. This is where consideration sets form.

Stage 3: Budget Framing

Price becomes the primary filter. The customer is still open about style but has defined what they're willing to spend.

"Best dress under $100?"
"What are good quality dresses in the $50–$150 range?"

Budget Framing is a commercial signal the AI reads clearly. Carousel trigger rate jumps to 88.8%. Brands whose product data cleanly maps to price brackets dominate.

AI behaviour: Price data quality matters enormously here. Brands with inconsistent, stale, or missing price data are filtered out regardless of brand recognition.

Stage 4: Attribute Constrained

The customer specifies two or more product attributes alongside a category.

"I need a floral, knee-length dress in cotton."
"Looking for a wrap dress that's suitable for an office setting."

This is the most data-dependent stage. The AI is matching product attributes to query attributes. Carousel trigger rate reaches 81.8%.

AI behaviour: Products with complete attribute coverage — material, occasion, fit type, care instructions — match these queries. Products without them don't appear, regardless of how well the brand performs elsewhere.

Stage 5: Comparison

The customer is weighing two or more specific options against each other.

"ASOS versus Reformation for summer dresses — which is better?"
"Lulus vs. Nordstrom: where do I get better quality for the price?"

Comparison intent is the most misunderstood stage. Carousel trigger rate drops sharply to just 15.3% — the AI usually answers in prose rather than product listings. But when it does surface products, brand diversity collapses to an average of 3.28 unique brands. The AI is making a recommendation, not showing a catalogue.

AI behaviour: Getting a favourable text mention at this stage — "Brand X has stronger fabric quality at this price point" — may be more valuable per mention than appearing in any product carousel. Brand-owned content (product pages, editorial, FAQs) is cited 6.1% of the time at this stage, the highest of any intent type.

Stage 6: Validation / Risk Reduction

The customer has a brand or product in mind but wants reassurance before committing.

"Is Reformation worth the price?"
"Are Lulus dresses good quality or do they fall apart quickly?"

This is the trust stage. The customer is one positive signal away from buying — or one negative signal away from walking. Carousel trigger rate is only 8.3%, meaning the AI almost exclusively answers in text.

AI behaviour: Brand-owned content, review data, and editorial coverage all influence what the AI says here. A brand that has no authoritative content addressing quality questions is effectively leaving this stage to chance.

Stage 7: Scenario Confirmation

The customer has a specific use case and wants to confirm which option fits best.

"Best dress for a beach wedding that isn't too formal?"
"What should I wear to a garden party in summer — smart casual?"

Scenario Confirmation is high-intent and highly specific. Carousel trigger rate is 80.8%. The AI is matching product attributes to a real-world context, not just a keyword.

AI behaviour: Brands with rich occasion and use-case tagging in their product data match these queries reliably. Brands without occasion metadata are structurally excluded.

Stage 8: Purchase Execution

The customer is ready to buy. They want to know where, how, and at what price.

"Where can I buy a Lulus midi dress online?"
"Buy floral wrap dress under $80 — where's the best option right now?"

This is the stage most brands focus on — and it does have the highest carousel trigger rate of any intent: 93.0%. But optimising for this stage alone assumes the customer already knows your brand. If they don't, they're not asking for you here.

AI behaviour: Urgency signals ("right now", "today"), deal language ("best deal", "cheapest"), and explicit purchase language ("buy", "where to purchase") each add 25–35 percentage points to carousel trigger probability.

Stage 9: Post Purchase

The customer has already bought and needs help — styling advice, care instructions, return guidance, or complementary items.

"How do I style a wrap dress for different occasions?"
"How do I wash a silk dress without ruining it?"

Carousel trigger rate is just 2.8% — the AI answers almost entirely in text. But this stage is not commercially irrelevant. Customers who receive useful post-purchase guidance from a brand's AI presence are building loyalty that feeds back into the next purchase cycle.

AI behaviour: Long-form content about product care, styling, and fit performs best here. Brands with this content establish themselves as the authority — which influences future recommendations.

The Journey in Practice: How a Real Customer Moves Through All Nine Stages

Consider a customer who starts by asking ChatGPT: "My dresses always look wrinkled by midday. What fabrics should I look for?"

That's Stage 1. The AI recommends polyester blends, ponte, and jersey knit — and mentions several brands whose dresses are made from these materials. The customer notes two or three names.

She comes back a few days later: "What are some reliable everyday dress options?" — Stage 2. The AI surfaces a broader set of brands, but the ones mentioned in Stage 1 already feel familiar.

Next: "Best midi dresses under $100?" — Stage 3. The price filter narrows the field. Brands with clean pricing data in that range appear.

Then: "I need a midi dress that's floral, knee-length, and machine washable." — Stage 4. Attribute matching. Only brands with complete product data make it through.

Later: "Lulus versus Nordstrom for everyday dresses — which is better quality?" — Stage 5. By now, Lulus has appeared at multiple prior stages. It's already in the customer's mental model. This comparison query isn't starting from scratch — it's resolving a preference the customer has been building since Stage 1.

Finally: "Where can I buy Lulus floral midi dress?" — Stage 8. Purchase intent. The brand winning here almost certainly won earlier.

This is why stage-by-stage visibility matters. Purchase Execution is where the conversion happens. But the decision was made across the seven stages that came before it.

Three Real Brands. Three Different Journey Profiles. One Clear Lesson.

We analysed brand visibility across all nine intent stages using our 8,520-query ChatGPT dataset — the same dataset behind our full brand visibility research. The results reveal three fundamentally different patterns.

Macy's: The Full-Funnel Retail Platform

Macy's appears as a product source in 24–33% of carousels across every intent stage from Problem Recognition through Purchase Execution. These are products from hundreds of brands listed on macys.com — not Macy's private-label products specifically. But the distribution illustrates what consistent, even coverage across intent stages looks like.

This is what full-funnel AI platform presence looks like. Whether a customer is recognising a problem for the first time or finalising a purchase, Macy's-hosted products are present. No single intent stage is dramatically weaker than another.

The even distribution reflects platform breadth — Macy's carries products across multiple tiers and occasions, so its inventory matches almost every query type. Compare this to Zara, which appears in fewer than 3% of carousels at most intent stages (100 unique sessions out of 5,072 triggered). Zara's one exception is Comparison queries, where it reaches 10.1% — suggesting ChatGPT recognises the brand but does not consistently source its products through structured feeds. Brand recognition and AI carousel presence are not the same thing. The reason is catalogue coverage, consistent price data across multiple tiers, and attribute completeness at SKU level. These are the inputs the AI reads regardless of how famous the brand is offline.

Walmart: The Budget Trap

Walmart's pattern looks nothing like Macy's.

The spike at Budget Framing (32.3%) is comparable to Macy's. But Category Exploration collapses to 5.2% — a 6.2x gap between the two stages. When a customer is just browsing dresses without any price filter, Walmart barely exists in AI search. The moment they add a price constraint, Walmart appears at full strength.

Walmart has one of the largest fashion catalogues in the US. But customers who don't open with price language — who are still exploring, still forming preferences — almost never encounter Walmart in ChatGPT's recommendations. Their consideration set closes before Walmart enters it.

This is the Budget Trap: appearing only when price is the explicit trigger, and being invisible for everything that happens before the customer knows what they want.

Lulus: The Discovery Gap

Lulus is the most carousel-present brand in our entire dataset — appearing in more unique shopping sessions than any other brand across 5,072 triggered carousels. If you read our brand visibility research, you already know Lulus outperforms Macy's, Nordstrom, and Petal & Pup on raw presence.

And yet, the intent-stage breakdown reveals a structural gap.

Lulus dominates mid-funnel and late-funnel. Its Category Exploration (34.6%), Budget Framing (31.0%), Attribute Constrained (33.1%), and Scenario Confirmation (34.4%) shares are comparable to Macy's platform numbers. But Problem Recognition drops to 15.1% — nearly half Macy's 24.0% at the same stage. And Post Purchase is zero.

The implication: customers whose journey begins with a problem — not a browse, not a budget search, not a scenario — are significantly less likely to encounter Lulus before they form their consideration set. By the time they reach the middle stages where Lulus excels, the window for early influence has already closed.

For the category leader, this is still a meaningful gap. For a brand with lower mid-funnel presence, the equivalent gap at Problem Recognition isn't a nuance — it's a structural exclusion from the journey before it begins.

Why Most Ecommerce Brands Have This Problem

The pattern we see across Walmart and Lulus — and dozens of other brands in the dataset — isn't random. It follows directly from how ecommerce brands have historically thought about digital marketing.

SEO content is largely transactional. Category pages, product pages, PDP optimisation — these target buyers who are already mid-funnel. They don't address the person who's still articulating a problem.

Paid search follows purchase intent. You bid on "buy midi dress" and "best dress under $100" — the equivalent of Stage 8 and Stage 3. Nobody is running campaigns for "why does my dress wrinkle so quickly."

Product feeds are structured for purchase. Price, availability, images. Not fabric care, occasion context, or problem-solving framing. The attributes that make a product visible at Stages 1, 2, and 6 are almost entirely absent from standard commerce feeds.

The result is a brand that is well-resourced at the bottom of the funnel and essentially invisible at the top — which, in AI search, means the AI has no reason to include you when a customer first starts their journey.

As one r/ecommerce commenter put it when discussing this exact problem: "AI keeps recommending my competitors but not us — how to fix this?" The answer, in most cases, isn't technical. It's content and data coverage across intent stages that the brand has never deliberately addressed.

What Each Intent Stage Needs From Your Brand

Showing up across all nine stages isn't a single fix. Each stage requires a different type of signal.

Where Does Your Brand Actually Stand?

Here is the honest version of the question most ecommerce teams aren't asking: across all nine intent stages, at what share of relevant AI shopping responses does your brand appear?

Not your Google rank. Not your paid search impression share. Your AI carousel presence, broken down by intent stage, with the gaps made visible.

Most brands don't know this number. They know they're "not showing up on ChatGPT" in a general sense — the way the r/ecommerce merchant knew their competitors were appearing but they weren't. What they don't know is which stages they're missing, which competitors are filling those gaps, and which specific changes to product data or content would shift the picture.

This is exactly what AEOsome measures. AEOsome runs structured queries across all nine intent stages for your category, records every carousel response, and maps your brand's visibility stage by stage — the same methodology behind the 8,520-query research dataset this post is built on. The output isn't a generic "AI visibility score." It's a specific, intent-by-intent breakdown that shows you where you're present, where you're absent, and where your competitors are taking the customers you never reached.

If Walmart's data looked like what we showed above, they'd know exactly where to focus: Stage 2. If Lulus's team saw their Problem Recognition gap, they'd know that informational content addressing early-stage customer problems is the next lever to pull.

Your brand has the same profile. You just may not have measured it yet.

Frequently Asked Questions

What are the 9 shopping intent stages in AI search?

The nine stages are: Problem Recognition (customer has a problem, no product in mind), Category Exploration (browsing broadly), Budget Framing (price is the primary filter), Attribute Constrained (two or more specific product attributes), Comparison (evaluating brands or products directly), Validation / Risk Reduction (seeking reassurance before buying), Scenario Confirmation (matching a product to a specific use case), Purchase Execution (ready to buy), and Post Purchase (help after buying). These stages were derived from analysis of 8,520 real ChatGPT shopping queries across the Women's Fashion > Dresses category.

Can a brand have good SEO and still be invisible in AI search?

Yes — and this is one of the most consistent findings in AI search research. A brand can rank on page one of Google for every relevant category keyword and still fail to appear in ChatGPT or Perplexity recommendations. AI search prioritises structured product data, attribute completeness, citation signals from retail domains, and content that directly addresses the query's intent — not keyword density or backlink counts. Brands built entirely on traditional SEO signals frequently have the AI visibility gaps described in this post.

Why does my brand only appear in ChatGPT when customers search by price?

This is the "Budget Trap" pattern. It usually means your product data is well-structured for price-constrained queries — pricing is clean, products fall clearly within common budget brackets — but your catalogue lacks the attribute depth, occasion coverage, and informational content that would make it relevant at earlier intent stages. The fix is adding coverage upstream: content that addresses Stage 1 (Problem Recognition) and ensuring product metadata covers Stages 4 and 7 (Attribute Constrained and Scenario Confirmation).

Why is the Purchase Execution stage not enough to focus on?

Purchase Execution has the highest AI carousel trigger rate of any intent stage (93.0% in our dataset). But it only reaches customers who already know what they want — and often, who they want it from. A customer who types "where can I buy a Lulus midi dress" has already decided on Lulus. Optimising only for Purchase Execution means you're visible to people who were already going to find you, and invisible to everyone still forming their preference. The decision happens at Stages 1 through 7. Stage 8 is just where it's executed.

What is AEO and how is it different from SEO for ecommerce?

Answer Engine Optimization (AEO) is the practice of ensuring your brand, products, and content are visible in AI-generated answers from tools like ChatGPT, Perplexity, Google AI Mode, and Copilot. Unlike SEO, which optimises for ranking in a list of blue links, AEO optimises for inclusion in synthesised recommendations where the AI selects a small set of brands to mention or show. For ecommerce, this means optimising product data, attributes, pricing, and content across all nine intent stages — not just the purchase-oriented queries that traditional commerce SEO targets.

How do I know which intent stages my brand is missing in AI search?

Manual testing — asking ChatGPT variations of queries across all nine stages — gives a rough picture but doesn't scale and isn't consistent across runs. A structured measurement approach runs hundreds of intent-matched queries for your specific category, records the output, and maps your brand's appearance rate stage by stage against competitors. AEOsome's research methodology does this systematically, using the same framework behind the 8,520-query dataset referenced throughout this post. The output is an intent-stage breakdown showing exactly where your brand is present, where it's absent, and which competitors are filling the gaps.

This analysis draws on 8,520 ChatGPT shopping intent queries executed against gpt-5-mini in the US market, covering the Women's Fashion > Dresses category. Queries were distributed across all nine intent stages and five query styles. Brand carousel share figures represent the percentage of triggered product carousels at each intent stage in which the brand appeared, normalised for differences in trigger rate and query volume across stages. Data was collected in February 2026.