What makes one listing easier for an agent to sell than another.
Given two nearly identical products, an AI agent recommends the one with fewer unresolved questions, not the one with better marketing copy. The gap between them is almost always one missing, narrow fact, not a missing feature.
6 min read
Small, checkable facts beat price or polish
Two phone cases, same price, both claiming MagSafe compatibility, sit a click apart in an agent's search. One retailer's listing just says "MagSafe compatible." The other says: compatible with MagSafe chargers up to 7.5W, tested on iPhone 15 and 16, magnets confirmed at full strength through the material. A buyer who specifically wants fast wireless charging gets a direct answer from the second listing and nothing but a claim from the first. The agent recommends the second, not because the case is better made, because the listing is.
It's tempting to assume an agent's recommendation comes down to price or reviews. Often it comes down to something much smaller: which of two otherwise-equivalent products answers the specific question this buyer's request depends on. A case that's £3 more but has a real answer to "will this actually hold a fast wireless charge" beats a cheaper one that leaves the agent guessing, because guessing is exactly what an agent recommending a real purchase is built to avoid.
The same gap shows up anywhere two products look interchangeable until one buyer's specific requirement breaks the tie: a rug listed as "non-slip" with no backing material named, against one that states plainly what the backing grips, or headphones marketed "noise-cancelling" with no attenuation figure against one that states a real decibel reduction.
The gap is usually one missing fact
Look closely at listings that lose out and the pattern repeats: it's rarely that the product itself is worse. It's that one specific, narrow fact never made it into structured data anywhere a system could check it. A wattage rating. A compatibility spec for a specific phone model. A material detail that determines whether something is machine washable. Small, specific, and precisely the kind of thing that gets left to a customer-service inbox instead of the product data.
Two sellers, one identical product, one still loses
The same gap shows up even when the product is completely identical: two retailers listing the exact make and model of portable speaker, the same manufacturer spec sheet, the same price. Neither is describing the item more or less accurately than the other, because there's nothing left to describe differently. What decides the recommendation there is which retailer has actually put the one fact a specific request turns on, the battery life in hours, the genuine IP rating, into a field an agent can read, rather than assuming the manufacturer's generic listing already covers it somewhere else. A retailer selling a commodity item doesn't compete on the product. It competes on whether its own copy of the shared facts is the version an agent can actually check.
Where this shows up for a business
Selfe surfaces the specific, narrow facts a real request turns on, straight from a retailer's existing product data, so a listing wins the recommendation on the fact itself rather than on how persuasively it's written.
Should we rewrite our product descriptions to sound more persuasive?
Not for this. What closes the gap for an agent is a specific, checkable fact, not more persuasive language.
How do we find out which fact is missing?
It's usually the one that ends up in customer-service emails after the sale, sizing, compatibility, material specifics, rather than anything already on the page.
Five questions an agent checks before it'll recommend a product.
An AI agent shopping on someone's behalf doesn't browse a product page the way a person does. Before it will recommend anything, it needs five separate questions answered: does the exact variant exist, is it currently in stock, will it work for this specific case, what happens if it's wrong, and will it arrive in time. A page built to persuade a browsing human answers few of those directly.
Returns and sizing: the two questions agents ask that catalogues rarely answer.
Sizing and returns are the two questions that decide whether an AI agent will risk a recommendation at all, and most catalogues answer neither with any real specificity. "True to size" and "easy returns" aren't answers, they're the absence of one.
What a product feed needs that a product page doesn't.
A product page is written for a person looking at one item. A product feed is what an AI agent reads to compare many at once, and it needs a different kind of precision: structured, decision-relevant fields, not the same copy reflowed into a spreadsheet.