# Product Titles That AI Agents Match > AI agents match shopper queries to product titles almost literally, so 'organic cotton weighted sleep mask' beats 'The Luna'. Lead with the words a shopper actually types. ## Key takeaways - Your product title is usually the first and most prominent text an agent reads, so leading it with the category, key attributes, and use-case makes the product legible to a query-matching agent. - In the ACES study the rewritten field is the product's displayed title/description text (one field in its mock storefront), and the paper describes the winning rewrites as title changes, with query keywords moved to the front of the title the most consistent driver. - Even so, the effect was concentrated (most product-and-model pairs saw no change while a minority saw large gains), so a title rewrite is a skewed bet, usually neutral and occasionally decisive. - Write titles in the words a shopper actually types, and keep the brand name after the descriptive words rather than in place of them. - Because the payoff is uneven, test a rewrite on your own catalog rather than trusting a formula. The research lab replicates the finding on live stores. ## How an agent reads your product title A shopping agent does not skim. It reads structured product data field by field and scores each product against what the shopper asked for. Your title is usually the first and most prominent text field in that record, so it does much of the work of telling the agent what the product actually is. The rest of the record ([your feed](/make-product-feed-ai-readable/) and [your product schema](/product-schema-for-ai-shopping/)) fills in the price, availability, and attributes around it. We read the public evidence to mean that an agent weighs how closely your product text answers the shopper's actual query, which puts a title written in the shopper's words at an advantage over one written in a brand's private language. Be precise about what that rests on: in the controlled evidence later on this page, the rewritten field is the product's combined title/description text (a single field in the study's storefront), and the paper itself frames the winning rewrites as title changes. What remains unmeasured is a separate title-versus-description comparison, and everything below keeps that line visible. ## The literal-match problem Human shoppers forgive a clever product name because they read the photo, the price, and the star rating around it and infer what the thing is. An agent resolving a query has less room for that inference. When a title omits the words a shopper used, it risks being scored as a weaker match to the query (the literal-match problem) because the agent has fewer cues to connect your product to the request. This is a claim about mechanism, consistent with the rewrite evidence below, and it is the crux of [agent selection](/glossary/#agent-selection) at the title level: be the text that most obviously answers the query. > Illustrative example, not measured data. A brand sells a weighted sleep mask under the name "The Luna." A shopper asks an agent for an "organic cotton weighted sleep mask for side sleepers." The title "The Luna" shares none of the shopper's words. A title like "Organic Cotton Weighted Sleep Mask, Blackout, for Side Sleepers" repeats the query's own language, and the brand can still ride along: "Luna Organic Cotton Weighted Sleep Mask, for Side Sleepers." The point is to lead with what the shopper searched, not with the codename. ## The title formula: category, attributes, use-case If the goal is to repeat the words a shopper is likely to type, a reliable ordering is to lead with the category, follow with the attributes that distinguish the product, and close with the use-case or fit. This is our recommended structure, not a rule any agent enforces: 1. **Category first.** The plain noun a shopper searches ("weighted sleep mask," "USB-C car charger"), so the product is legible before any modifier. 2. **Distinguishing attributes.** Material, size, colour, capacity, or compatibility: the words that separate your product from the rest of its category ("organic cotton," "65W," "blackout"). 3. **Use-case or fit.** Who or what it is for ("for side sleepers," "iPhone 15/16"), which is often exactly how a shopper phrases the underlying need. 4. **Brand where it helps, not instead.** Keep the brand name if it earns trust, but let it follow the descriptive words rather than replace them. The same title then feeds every engine that reads your catalog, so the fix is write-once. How a given platform surfaces that title differs. The [ChatGPT shopping playbook →](/chatgpt-shopping-optimization/) covers applying this where OpenAI's agent reads your feed. ## Before and after: title rewrites The formula is easier to trust once you see it applied. Each pair below takes a title written in a brand's private language and rewrites it in the words a shopper types, leading with the category and the attributes that set the product apart. We expect the rewritten titles to answer more queries, because every descriptive term you add (material, size, capacity, compatibility, fit) is one more attribute an agent can check against a request, while a bare model name offers almost none. The brands, models, and specs below are invented to keep the pattern clear. > Illustrative example, not measured data. A cookware listing titled "Norvane Signature 10" gives an agent a brand and a model number and little else. Rewritten as "Norvane Nonstick Ceramic Frying Pan, 10-Inch, PFAS-Free, Induction Compatible," it names the pan type, the coating, the size, the safety property, and the stovetop compatibility, so a query for an "induction-safe nonstick pan" has concrete attributes to match instead of a code. "Auralen Drop 3" names a product without saying what it is. "Auralen Wireless Noise-Canceling Earbuds, Bluetooth 5.3, 30-Hour Battery, IPX4 Water Resistant" keeps the brand but leads with the category and then the specs a shopper filters on, so a request for "noise canceling earbuds with long battery life" can line up against the connectivity version, the battery figure, the noise-canceling feature, and the water-resistance rating. "Ostwick Executive" reads as a trim name, not a category. "Ostwick Ergonomic Mesh Office Chair, Adjustable Lumbar Support, 300 lb Capacity" turns that codename into the chair type, the back material, the ergonomic feature, and the weight rating, so a shopper asking for an "ergonomic office chair with lumbar support" finds four matchable terms where there were none. "Trailwen Summit" tells an agent nothing about the footwear. "Trailwen Waterproof Hiking Boots, Men's Wide Fit, Ankle Support" surfaces the boot type, the waterproofing, the gender and fit, and the support feature, so a query for "wide waterproof hiking boots" can match on each detail without inferring it from a name. The move is the same in every pair: nothing about the product changed, only how legibly the title states what it is and how many of its attributes an agent can pair with a query. ## The evidence: text rewrites shifted selection The reason to take title wording seriously is that changing product *text* measurably moved which product an agent picked in the strongest public study we have. One structural fact matters throughout: in the ACES storefront, a product's displayed title and description are a single text field, so the paper uses "title" and "description" for the same rewritten text. In the ACES simulation, rewriting a product's description to better fit the shopper's query raised its market share in the agents' picks by an average of +3.66 percentage points for Claude Sonnet 4, +8.37 pp for GPT-4.1, and +14.79 pp for Gemini 2.5 Flash, all statistically significant. ACES is a controlled simulation (vision-language-model agents choosing from a mock storefront in randomized trials, not a record of real purchases on live stores), so read these as evidence that text *can* move selection, not as guaranteed conversion math for your catalog. From there, the step to titles is shorter than we once framed it, because the paper itself makes it. In ACES the rewritten field is the product's displayed title/description text, a single field; the paper's current revision instructs its seller agent to change the product title without inventing features or adding spurious keywords, describes the winning rewrites as title edits that can materially shift selection shares, and identifies relocating the query's own terms to the beginning of the title (keyword front-loading) as the most consistent driver of the gains. One caveat stays ours: ACES never ran a title and a long-form description as two separate fields, so a field-versus-field comparison remains unmeasured. What the evidence shows is that rewriting the product's title text moved selection. ## Why the effect is concentrated, not uniform A rewrite is not a lever that pays out every time. The description-rewrite effect was concentrated rather than uniform: in 67% of the category-model pairs a one-shot rewrite produced no significant change, while 33% produced large gains. Where it did land, the upside was real: the biggest outliers reached +52.0 pp for an office lamp on Gemini 2.5 Flash and +30.3 pp for an office lamp on GPT-4.1. For a title that means the expected value is skewed: most rewrites will do little, a minority will do a lot, and you cannot tell in advance which product falls where. Treat a query-aligned title as a low-cost bet with an occasional large payoff, not a reliable percentage lift you can bank across a catalog. ## From a title to a test Because the payoff is uneven (mostly neutral, sometimes large), a title rewrite is a hypothesis about your specific catalog, not a guarantee, and the only honest way to know is to measure it. AgentMint.net's research lab is replicating the ACES description-rewrite finding on live catalogs, with results held in a "data collection in progress" state until real numbers exist; the design is written up in [do description rewrites shift AI recommendations? →](/research/description-rewrite-experiment/). Start there before you rename a catalog on faith, and read [how AI shopping agents choose products →](/how-ai-agents-choose-products/) for the full set of signals a title sits inside.