SEO Archives - Emrise Digital

Why your product data is losing you sales

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.

Woman using a smartphone and credit card to shop online

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.

Hands typing on a laptop with a data dashboard on screen

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.

Laptop on a desk displaying an ecommerce product listing page

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.

Your customers are using AI to shop. Is your store ready?

Most e-commerce brands are still optimising for a customer journey that fewer and fewer people are actually taking. The assumption of browse, search, compare, click, buy made sense for a long time. That is changing, and faster than most brands have accounted for.

A growing share of your customers are now starting that process somewhere else entirely: inside an AI. They’re asking ChatGPT what to buy for a wedding anniversary, letting Perplexity compare luxury candles and asking Gemini to find the best-reviewed gifts under £100. For most shoppers today, AI is still playing an advisory role. It surfaces options, compares products and shortlists choices before the customer makes the final decision. But the share of that decision-making happening inside AI tools, rather than on your store, is growing fast.

This isn’t a prediction. It’s already happening, and most brands have two significant blind spots at once.

The discovery problem

AI shopping agents don’t browse the way humans do. They don’t scroll category pages or respond to homepage banners. They pull structured data, parse product feeds and make decisions based on what they can actually read and verify. If your product information is incomplete, inconsistent or missing key attributes, agents may not flag it. They move on, and your competitor gets surfaced instead.

The scale of this shift is already showing up in traffic data. AI-referred traffic to retail sites grew 693.4% across the 2025 holiday season, according to Adobe Analytics, though it’s worth noting this is from a low base. eMarketer forecasts AI platforms will drive $144 billion in e-commerce sales by 2029, nearly 9% of all US retail e-commerce. eMarketer also projects that 20% of all online transactions could be initiated by AI agents by the end of this year, a figure echoed by Gartner, though “initiated” covers a wide range of behaviour, from an AI recommendation that leads to a click through to fully autonomous purchasing, which remains rare.

A joint study by IBM’s Institute for Business Value and the National Retail Federation, surveying 18,000 consumers across 23 countries, found that 45% are already using AI somewhere in their buying journey. Adyen’s 2026 UK Retail Report found that 44% of UK shoppers are now open to having AI handle the entire shopping process, including the final purchase, once their preferences and budget are set. This is a shift still in progress, but the direction is clear.

A meaningful and fast-growing portion of your addressable market is making purchase decisions in an environment where your product content needs to work harder than ever.

The brands currently performing well in these environments aren’t necessarily the biggest or the best-known. They’re the ones whose data is structured, complete and consistently maintained across every feed and platform. Missing dimensions, vague product descriptions, absent review data and unstructured specifications are no longer just bad for traditional SEO. As AI search becomes a more significant part of how products are found and evaluated, the stakes attached to data quality have risen considerably.

Maintaining that consistency across feeds, platforms and a constantly changing product catalogue is demanding but increasingly essential.

Person browsing ecommerce products on a laptop.

The experience problem

The expectations of customers who still land directly on your store are also shifting.

People who regularly use AI tools in their daily lives, for research, for recommendations, for planning, are increasingly accustomed to experiences that feel relevant to them specifically. Not just “personalised” in the sense of a name in an email subject line, but genuinely tailored. Recommendations that reflect their actual preferences, interfaces that surface what they’re likely to want before they go looking for it.

When those customers arrive on an e-commerce site that doesn’t match that experience, the gap is noticeable. According to Adobe Analytics, visitors referred from AI platforms convert at a rate 31% higher than average and generate 254% more revenue per visit, suggesting that when the experience matches the expectation AI has already set, shoppers are significantly more ready to buy. Customers who don’t get that experience are increasingly likely to leave without converting, having been conditioned by AI-assisted experiences to expect something better.

This isn’t a feature problem. It’s a foundations problem.

Brands that are getting this right aren’t deploying one big solution. They know what a returning customer looks like, and they can adjust what products are shown and in what order.

Woman with a book using a mobile phone for research.

The visibility gap

What makes this difficult is that the signals don’t always show up where you’d expect them to. AI referral traffic, Google AI Overviews and brand visibility across recommendation platforms are increasingly trackable, but the picture is rarely complete, and interpreting it requires a different kind of attention than reading a standard analytics dashboard.

The earlier part of the funnel is harder still. When AI tools evaluate products during the recommendation phase, that process doesn’t show up in your usual reports. The gap tends to surface indirectly, in conversion rates that don’t quite add up or revenue that’s hard to account for directly.

As McKinsey notes, most brands are still running personalisation efforts as manual, one-off experiments rather than measuring them in any integrated way, which means the effect often gets absorbed into overall performance numbers without a clear cause.

What ties all of this together is the quality of the foundations underneath. Structured product data, clean feeds, solid SEO and proper analytics infrastructure are what influence visibility in AI search and traditional search alike. The brands least likely to face these problems are those who’ve already got that groundwork in place.

These things compound over time. AI systems learn which products and brands to surface based on data quality and engagement signals. Stores that aren’t visible or competitive in AI-referred channels today will become progressively less so. The window to build the right foundations is narrowing.

It is also where the right expertise makes the most difference.

AI hasn’t changed what good e-commerce looks like at its core: relevant products, trustworthy information, a smooth path to purchase.

Emrise works with e-commerce brands on the foundations that influence both search visibility and AI recommendation, from structured product data and technical SEO to analytics and measurement. Get in touch to find out where your store stands.