A customer emails to say the ring they ordered looks nothing like the photos. A return lands back in the warehouse. A one-star review goes up. These things hurt, but at least they are visible.
What’s harder to see is the business you lose quietly. The shopper who landed on your product page, found the description too vague to commit and went elsewhere. The product that was excluded from Google Shopping because of a data error in your feed. The item that never surfaced in an AI recommendation because it was missing the attributes the system needed to evaluate it.
Most e-commerce brands think about product data as an operational matter: something to manage, keep tidy and revisit when there is time. In practice, product data quality is a commercial variable and for many brands one of the most significant sources of preventable revenue loss.
The cost of poor product data
The clearest evidence of the commercial cost sits at the customer level.
Research from Salsify’s 2025 Consumer Research report, based on a survey of around 2,000 shoppers across the US and UK, found that 71% have returned a product because it did not match the online listing and 54% have abandoned a purchase because product content was inconsistent across channels. Research reported by Retail Times in March 2026 tells a similar story: 43% of consumers returned a product in the past year because pre-purchase information was incorrect and 68% said they would stop buying from a brand altogether after a bad product information experience. These are not isolated incidents. They are patterns of behaviour that more than half your potential customers have exhibited at some point in their buying journey.
For gift and jewellery brands in particular, where customers are often buying for someone else and cannot inspect the product in person, accurate and consistent product content is doing much of the work of a physical retail environment. A vague description, an inconsistent size guide or a photo that misrepresents colour or finish does not just create friction. The result is either a lost sale or a return that eats into your margins.
A return driven by inaccurate data costs twice: once in logistics and once in trust. For gift and jewellery customers, that first purchase should be the beginning of a longer relationship. Losing it to a misleading product description is entirely avoidable.
How product data affects your visibility
Beyond the customer-facing impact, product data quality has a direct bearing on whether your products are visible in the channels where purchase decisions are increasingly being made.
Google Shopping operates on the accuracy and completeness of the product feed you submit to Merchant Center. Analysis by DataFeedWatch, published by Search Engine Land in 2023, found that around 7% of all products submitted to Google Shopping are disapproved due to critical errors and that invalid Global Trade Item Numbers (GTINs), the unique numerical identifiers that Google uses to match your products to its catalogue, affect 48% of all merchants. Inadequate product data is more common than most brands realise and quietly removes products from paid and organic shopping placements. Left unresolved, it can affect the performance of your entire store.
The issue extends well beyond GTINs. Missing or incorrect product attributes, mismatched pricing, image policy violations and incomplete category data all contribute to products either being disapproved outright or performing significantly below their potential. A product that exists in your catalogue but fails to meet feed requirements will not appear in results.
The same logic applies to AI-powered discovery. As we explored in our recent piece on AI shopping behaviour, AI recommendation systems rely heavily on structured and complete product data to evaluate, compare and surface products accurately. They do not browse visually in the way a human would, and incomplete or missing attributes make accurate assessment significantly harder. If your product feed is missing key attributes, competitors with cleaner, more complete data are more likely to be surfaced instead. The quality bar for AI visibility is higher than for traditional search because there is no human judgment to fill in the gaps.
The technical foundations that support feed quality, page speed, structured content and mobile responsiveness are the same ones that underpin strong SEO performance more broadly. Our guide to SEO web design covers those principles in more detail.
The scale of the problem
Most brands assume their product data is largely in order. The evidence suggests otherwise.
Research from Akeneo, based on a survey of 650 business leaders across the US, UK, France and Germany conducted in 2024, found that 99% of organisations face at least one product information challenge. That figure is striking not because of the scale but because of the universality. The issue is not confined to brands that have neglected their data infrastructure. It affects organisations that are actively managing their product information and still find it wanting.
For smaller and mid-sized brands without dedicated data teams, the picture is often more pronounced. Product information tends to accumulate in disconnected places: a spreadsheet here, a supplier document there, descriptions written in haste at launch and never revisited. As catalogues grow and channels multiply, the inconsistencies compound. The gap between what a brand believes its product data looks like and what is actually being read by Google’s feed validators or by an AI recommendation system is often significant.
Good data is the foundation
Product data quality is not a technical problem sitting to one side of your commercial activity. It is load-bearing. It determines whether customers trust you enough to buy, whether your products appear in shopping feeds and whether AI systems can find and recommend them.
In our recent piece on AI shopping and e-commerce visibility, we explored how AI agents evaluate products during the recommendation phase and how brands with structured, complete data are already at an advantage. The same foundations that improve AI visibility also improve feed performance, on-site conversion and long-term customer trust.
Most brands that look closely at their product data find more gaps than they expected. The good news is that they are fixable, and addressing them has a measurable effect on visibility, conversion and returns. That is where the work pays off.
If you’d like a clearer picture of where your data stands, get in touch.
