{"id":26453,"date":"2026-10-29T12:12:51","date_gmt":"2026-10-29T12:12:51","guid":{"rendered":"https:\/\/cosmo5.com\/?p=26453"},"modified":"2026-10-29T12:12:52","modified_gmt":"2026-10-29T12:12:52","slug":"orthogonal-data-what-ai-cannot-guess-about-your-products","status":"publish","type":"post","link":"https:\/\/cosmo5.com\/en-gb\/orthogonal-data-what-ai-cannot-guess-about-your-products\/","title":{"rendered":"Orthogonal data: what AI cannot guess about your products"},"content":{"rendered":"
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On ChatGPT, Gemini or Google’s AI Mode, users no longer search for a “cashmere sweater” but for a “cashmere sweater that does not pill”. Traditional product pages are no longer enough to answer this kind of request. Competitive advantage is shifting toward another category of information: data that AI can neither infer nor invent orthogonal data. Here is why.<\/em><\/p>\n<\/div>\n\n

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Ask an AI assistant for “a cashmere sweater that does not pill”. It will confidently name a few options. But what is it basing its answer on? No brand states on its product page that its sweater “does not pill”, and no one makes any promises about how knitwear will hold up over time.<\/p>\n<\/div>\n\n

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In the absence of official information, the model adapts. It draws on customer reviews, Reddit threads and blogs, in other words, on anecdotal and haphazard data. At best, it reasons from technical specifications, where these are available: fiber diameter and length, origin (cashmere from Inner Mongolia is reputed to pill less) and two-ply construction.<\/p>\n<\/div>\n\n\n

Structured product data: necessary but insufficient<\/h2>\n\n\n
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For fifteen years, e-commerce has relied on structured product data. With its taxonomy and fields (title, category, price, brand, color, size, material, availability, etc.), the Google Shopping feed has become the de facto minimum standard. And rightly so: for most mature e-commerce businesses, improving the reliability of this feed remains one of the investments with the highest marginal returns, because it is used across all channels and every improvement has a cascading effect.<\/p>\n<\/div>\n\n

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One strength of this model is that it is perfectly suited to conventional “noun + adjective” queries, such as “cashmere sweater”, “gray sweater” or “SPF 15 serum”. But the arrival of AI search engines is changing the landscape.<\/p>\n<\/div>\n\n

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AI can now extract these attributes on its own. If a description says “fine knit in 100% cashmere”, an LLM identifies the material even if the dedicated field has not been filled in. If the title mentions the shade, the color is inferred. Google Shopping is increasingly filling in missing fields automatically, and new advertising platforms such as ChatGPT Ads extrapolate whatever can be inferred. In other words, basic product data feed hygiene remains a prerequisite, but it is no longer enough to stand out.<\/p>\n<\/div>\n\n\n

Users’ search queries are changing<\/h2>\n\n\n
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Another, even more profound shift deserves the attention of marketing and e-commerce teams: with conversational AI, the queries themselves are changing. According to Google, questions asked in AI Mode are, on average, three times as long as traditional search queries.<\/p>\n<\/div>\n\n

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It might seem that users are simply stringing together more adjectives. But that is not the case: they are moving from a “noun + adjective” approach to a “concept + constraints” approach. For example, when using AI, users no longer search for “a cruelty-free SPF 15 serum foundation”, but for “the cheapest cruelty-free serum foundation with an SPF above 15”. They are no longer looking for “black leather boots”, but for “a pair of black leather boots that are comfortable enough to walk in all day and stand up to rain”.<\/p>\n<\/div>\n\n

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A conventional query can be answered by reading fields, whereas a “concept + constraints” query requires reasoning: finding the lowest price, accepting a whole range of SPF values, recognizing that a label such as “Leaping Bunny” indicates “cruelty-free” and, in the case of “comfortable all day”, looking for information that simply does not exist on the product page. Yet AI systems would rather give a poor answer than no answer at all, so users are getting used to these complex queries even when the data is missing.<\/p>\n<\/div>\n\n

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To make AI results more reliable, Google has started to respond by adding conversational attributes to Merchant Center, including a Q&A field. This is a useful solution, but it is still not enough. Without a method for identifying what should go into these questions and answers, most companies recycle what they already publish. They would be better off putting their efforts into building databases of orthogonal product data.<\/p>\n<\/div>\n\n\n

What is orthogonal data?<\/h2>\n\n\n
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Orthogonal data is information that cannot be inferred, extrapolated or deduced from what has already been published about a product. It is new to consumers, as well as to LLMs, search engines and all other discovery tools.<\/p>\n<\/div>\n\n

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There is a simple test to determine whether data is truly orthogonal: could an AI model derive this information from my current product page? If so, it is not orthogonal. Material, color or size, for example, can be deduced. But the abrasion resistance of a pair of pants, the noise level of a rice cooker or the doorway width needed to fit a sofa through cannot.<\/p>\n<\/div>\n\n

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This data offers brands several advantages: it reduces hallucinations by AI assistants, expands the range of queries a product can answer and improves visibility in conversational search engines and new search interfaces. Above all, it is much harder for competitors to replicate!<\/p>\n<\/div>\n\n\n

New data to collect and add<\/h2>\n\n\n
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Unfortunately, the most useful data is often unavailable, expensive to gather or impossible for the brand itself to publish because of reputational or legal liability concerns.<\/p>\n<\/div>\n\n

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Nevertheless, several options are available. For brands, a first step is to finally bring to light the data lying dormant in their internal systems (PIM, DAM, ERP, CMS). Retailers, meanwhile, need to step up their efforts when adding products to their catalogs. Some already use computer vision during product photo shoots to identify characteristics that manufacturers do not provide.<\/p>\n<\/div>\n\n

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At the same time, editorial partnerships can be another way to enrich product information, particularly in sectors with specialist media and enthusiast communities. Specialist sites that test, measure and describe products in detail hold particularly valuable information: some cut open athletic shoes to measure the thickness and density of the sole; others monitor a mattress’s temperature throughout the night, record the sound of keyboard keys or assess their haptic feedback… These are all measurements that no product page includes and no model can deduce.<\/p>\n<\/div>\n\n

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Another option is user-generated content, which is still too often underused. It is the ideal way to surface information that is in high demand but that the brand cannot state itself. Making better use of user-generated content can take several forms: aggregating reasons for returns and customer service inquiries to enrich the product page (“this model runs small”), rewarding customers who document the product in use through photos or videos, collecting information verified by customers, and so on.<\/p>\n<\/div>\n\n

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These approaches all deserve a place on a 2027 roadmap. Because AI cannot guess what it does not know, orthogonal data has become the new battleground in e-commerce. And everyone has a role to play. Brands alone hold much of this information about their own products. Multibrand retailers, meanwhile, are the only ones able to remain neutral and cover the entire product range, including the data that helps distinguish between two products\u2026 and therefore identify the inferior one, something no brand will ever do with its own products.<\/p>\n<\/div>\n\n

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Contact us<\/a><\/strong><\/p>\n<\/div>\n\n

<\/div>\n\n","protected":false},"excerpt":{"rendered":"

On ChatGPT, Gemini or Google’s AI Mode, users no longer search for a “cashmere sweater” but for a “cashmere sweater that does not pill”. Traditional product pages are no longer enough to answer this kind of request. Competitive advantage is shifting toward another category of information: data that AI can neither infer nor invent orthogonal […]<\/p>\n","protected":false},"author":32,"featured_media":600,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[15],"tags":[],"content-scope":[],"featured":[],"industry-tag":[],"market-country-tag":[],"partner-tag":[],"service-tag":[],"category-type":[],"class_list":["post-26453","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-article-insight"],"acf":[],"yoast_head":"\nOrthogonal data: what AI cannot guess about your products | Cosmo5<\/title>\n<meta name=\"description\" content=\"Discover how\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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