AI shopping assistants read a product sheet like a data feed, not a storefront. Here’s how to standardize your catalog attributes to remain agent-selectable.
A new buyer enters your online store. It ignores polished visuals and brand storytelling. It queries your catalog like a database: attributes, prices, availability, compatibility. This buyer is an AI, and he is more and more often interposed between the merchant and the end customer.
The buyer is no longer always human
Your best customer may no longer have a brain. He has a prompt.
For twenty years, e-commerce has optimized for one judge: the human eye. Nice photo. Catchy title. Description that tells a story. Everything converged on a decision made by a brain, with its emotions and biases.
A new category of buyer is arriving. She doesn’t feel anything. ChatGPT Shopping, browser-integrated assistants, agents who compare and pre-select: these tools slip between the customer’s request and your product sheet. The customer expresses a need. The agent does the shopping for him. He reports a short list. The customer decides from this list.
The merchant no longer directly convinces the buyer. He must first convince the machine that selects. And this machine doesn’t read a file. It analyzes it as a data endpoint.
The question is no longer “does my profile make you want it?”. It becomes “is my file readable by an agent who compares it to a hundred others in a fraction of a second?”
What an agent really reads
A hypothetical case. A customer asks his assistant: “find me a lightweight cordless drill, 18 volt battery, compatible with my brand X batteries, deliverable within 48 hours, less than 150 euros.”
The agent does not admire the photo of the drill on a construction site. He’s looking for matches. Weight. Tension. Compatible battery brand. Deadline. Price. Five attributes. Five filters.
If these five pieces of information exist in usable form, the product enters the selection. If one of them is buried in a marketing paragraph, or absent, the product goes out. The agent doesn’t guess. He does not deduce that an “ultra-handy” drill probably weighs little. It needs a value, a unit, a named attribute.
For a human, these elements are reconstituted by context. For an agent, what is not declared does not exist.
An agent first reads the structured: tagged data, named attributes, feature tables. The free text comes later, to remove ambiguity, never to carry the main information.
Normalize, and only then seduce
The solution can be summed up in one word: normalization. A standardized attribute is named data, with a constant value, unit, and format throughout the catalog.
Take the color. “Midnight blue”, “navy blue”, “navy”, “dark blue” designate similar shades. For a human, the gap is trivial. For an agent who filters on color, four labels fragment the data into four. A single repository solves the problem. Same logic for sizes, materials, capacities, dimensions.
The weight is always expressed in the same unit, in a dedicated field. The dimension follows a constant format. Compatibility lists precise references, not vague formulas. Availability reflects actual, up-to-date status. The price is clear, with no conditions hidden behind a click.
The tools already exist. The structured data recognized by the engines declares a product, its price, its availability, its characteristics, in a language that the machines read. Google Merchant Center has imposed a discipline of attributes for years: title, status, availability, brand, identifiers. This rigor now serves purchasing agents.
A merchant who already maintains a clean feed has a head start. The logic is identical: explicitly declare, in a stable format, what an automated system must read without interpreting.
Which causes a product to be ruled out.
An agent eliminates faster than he chooses
- The missing attribute. The client poses a constraint. The sheet does not display the information. The agent does not take the risk of including the product. He prefers a competitor who declares the data. Lack of information equals lack of compliance.
- The inconsistency. The title says one thing, the description another, the structured field a third. A weight that changes from one place to another. “In stock” availability contradicted below. The agent perceives the contradictory signal and lowers its confidence in the entire file.
- The data is out of date. An outdated price, false availability. For an agent who must present a reliable list, this is a risk that he avoids.
- Information locked away. A characteristic only present in an image, a compatibility table available only in PDF. What is not structured text remains, for many agents, invisible.
None of these reasons relate to the actual quality of the product. An excellent article may be rejected because its entry is illegible for a machine. The loss is silent. The merchant does not see the sale go through because he never had the opportunity to dispute it.
The order of priority
Structuring a catalog of thousands of references at once is rarely realistic. Better a clear order.
- First the filter attributes. The criteria on which the customer places a firm constraint: price, availability, size, capacity, compatibility, dimensions. They decide on entry into the selection. Without them, the rest is useless.
- Then internal consistency. Check that the structured field, table and text tell the same story. A single version of each data, repeated identically everywhere.
- Then the freshness. Keep price and availability in sync with reality. Perfect but false structured data does more harm than absent data: it betrays the agent’s trust.
- Finally, descriptive richness. Secondary attributes that refine a close comparison. They do not decide entry into the list, but they can influence a final arbitration.
We first secure what makes it selectable, then what makes it credible, finally what makes it stand out. Enriching marketing prose before having its own attributes is like decorating a door that the agent cannot open.
Two layers, not a sacrifice
Should we give up storytelling and emotion to only serve machines? No.
The end customer remains human. It is he who decides on the short list that the agent presents to him. The beautiful photo, the clear title, the reassuring description retain all their value at this precise moment. What changes is that they intervene after an initial algorithmic selection.
A structured, standardized layer decides whether the product enters the race. A sensitive editorial layer helps make the final decision. The first precedes the second, it does not replace it.
The merchant who understands this sacrifices nothing. It slips a data discipline under a window that remains attractive. The product becomes findable by the machine and desirable for humans.
A question of hygiene, not technology
Nothing here is technical prowess. No model to train, no rare tool to deploy. The building blocks exist: structured data recognized by the engines, disciplined product flows, consistent attribute repositories. The difficulty is not technological. It is organizational.
Who owns the reference color? Who guarantees that the weight is always declared in the same unit? Who synchronizes availability? These responsibilities, diluted between several teams and several tools, are the real work.
The buying agent is not asking for anything extraordinary. It requires clarity, constancy, freshness. Exactly what a good catalog should always offer, and which the new buyer is finally making a priority.
If a buying agent searched your catalog tonight, how many of your products would even make it through their selection door?
