An online retailer invests in the most advanced search technology on the market: AI-powered, semantic, lightning fast. And yet a customer still can't find a particular power drill, even though it has long been part of the range. The reason rarely lies in the search technology itself. It usually lies one level deeper: in a product record that contains neither a power rating nor an application area nor a second synonym – the one the customer actually searched with. The best search in the world cannot find a product it doesn't understand, and it only understands a product as well as the underlying data allows.
This connection is underestimated in many e-commerce projects. Companies spend a lot of money on search algorithms and pay comparatively little attention to what is supposed to give those algorithms meaning in the first place: clean, complete, consistent product data. Yet that is precisely the lever that turns an average search function into a real revenue driver.
Why search is only as good as the data behind it
From a technical perspective, search functions, filters and product recommendations are nothing more than evaluations of product attributes. If those attributes are missing or incompletely maintained, the shop system simply cannot analyse them – which means search, filters and recommendations will, in case of doubt, not work optimally.1 A product with no material specification on file will not appear when customers filter by material. An item without consistent units of measurement cannot be properly compared within its product group. And a record that sits in the system twice as a duplicate misleads both customers and the retailer's own product management, for instance regarding availability.1
At first glance these problems look small and technical. Taken together, however, they determine whether a customer ever sets eyes on the product they were looking for – long before price, design or shipping costs come into play at all.
What poor product data really costs
A study by ECC Köln in cooperation with Atrify shows how expensive this becomes in practice: in fast-moving consumer goods (FMCG) in particular, 68 percent of customers who abandoned an online order in the past twelve months cite missing or incorrect product information as the reason.2 Around 77 percent of respondents also agree that they would rather order from a shop that provides comprehensive product information.2 Another survey, conducted by the Initiative Digitale Handelskommunikation, paints an equally clear picture: if a desired piece of product information cannot be found anywhere, almost half of respondents – 49 percent – prefer to buy something else or nothing at all.3
These figures make one thing clear: poor product data doesn't just cost you individual clicks in search. Ultimately it costs entire purchase decisions – and precisely among those customers who were already ready to buy. What makes it particularly painful: these customers generally give no feedback and don't complain. They close the tab, move on to the next search result and buy where the information is more complete – without the original shop ever finding out why the revenue failed to materialise.
The dimensions of good product data
For product data to reliably feed a search function, it has to meet several criteria at the same time. Completeness means that every attribute provided for in the data model is meaningfully filled in for every product, not just title and price. Consistency means that units of size, designations and categorisations are used uniformly across the entire range, so that customers can filter and compare in a targeted way within a product group at all. Uniqueness means that no duplicate or contradictory records for the same product are circulating in the system.1 If any one of these three dimensions is missing, it directly affects findability and the user experience – regardless of how advanced the search technology running in the background actually is.
This becomes especially apparent with technical assortments, for example in B2B trade with spare parts, machine components or building materials. Here, a single missing attribute – the strength class of a screw, say, or the voltage rating of a component – is often enough to make an otherwise perfectly matching query come up empty. The more granular and technical an assortment is, the greater the effect clean data maintenance has on result quality – and the more expensive every gap that goes undetected becomes.
The business case: what pays off
The topic becomes truly interesting when you look at what structured product data maintenance actually achieves. According to figures from Forrester Research, companies that consistently use a product information management (PIM) system save up to 50 percent of the time spent on data maintenance and generate up to 20 percent higher revenue.4 A concrete example from practice backs this up: at a mid-sized brand manufacturer, structured data management reduced the maintenance effort per product from 17.5 to 4.5 hours – a time saving of 73 percent, equivalent to roughly 845 euros saved per product per year. In a fully calculated scenario with 10 million euros in annual revenue, this led to a 47 percent increase in profit.5
That there is still considerable potential across the industry is shown by a recent DIHK survey of 5,000 German companies: 76 percent of the SMEs surveyed are currently struggling with inadequate data quality and data silos – by their own assessment the most uncomfortable and, at the same time, most frequently ignored figure in the entire survey.6 Anyone who closes this gap is therefore not working on a niche topic, but on a problem that the vast majority of their competitors have yet to solve either.
How Eywora makes product data quality visible and manageable
This is exactly where Eywora's Health Score module comes in. Instead of treating product data quality as a separate IT project that gets postponed for years, Eywora continuously assesses every single item in the catalogue against several quality dimensions and reveals exactly where data gaps are limiting a product's findability. Rather than an abstract list of errors, the team receives recommendations prioritised by impact: which correction has the greatest effect on visibility and revenue, and in which order is it most worthwhile to work through them?
Corrections can be made directly in the platform and are automatically fed back to the shop – with no new feed upload and no developer resources required. Because search, Health Score and analytics all work on the same data basis, the effect of an improved product description shows up not only in the Health Score itself, but immediately in search performance and in the revenue contribution of the queries concerned. In this way, an often neglected data maintenance task becomes a measurable, controllable lever for conversion.
If you want to see how many of your own products currently suffer from data gaps, you can test Eywora on your own catalogue free of charge for 14 days without a credit card, or book a 30-minute live demo using a real customer shop.
A practical roadmap: where to start
If you don't know where the biggest data gaps in your catalogue are, don't start with your highest-revenue products but with those that are searched for most often yet rarely found. A look at your own search logs usually shows quickly which queries regularly lead to zero results or to obviously unsuitable ones. In the vast majority of cases the cause is not the search technology but missing attributes, inconsistent designations or outdated descriptions for exactly those items. Tackling these first delivers the fastest visible effect from better data maintenance – and makes the business case easiest to prove internally.
Conclusion: data maintenance isn't a hygiene factor, it's a revenue strategy
The idea that product data maintenance is a necessary evil in the backend that will "have to be dealt with at some point" doesn't hold up in reality. It is the foundation on which any search technology can work in the first place, and it is directly and measurably tied to revenue lost or won. Companies that maintain their product data consistently don't just improve their search; they improve their entire customer journey, from the first query to checkout. Anyone who still believes in 2026 that better search technology alone will solve the findability problem is overlooking the actual cause: it isn't that search understands too little, it's that the data tells it too little.
Sources
- marconomy – Product data – the Achilles heel of e-commerce ↩
- onetoone.de – Well-maintained product data prevents purchase abandonment, citing a study by ECC Köln and Atrify ↩
- Händlerbund – Missing product information causes half of shoppers to abandon their purchase, citing a survey by the Initiative Digitale Handelskommunikation (IDH) ↩
- Smart Commerce – PIM & MDM, citing Forrester Research ↩
- onacy – PIM and PXM: What the numbers really say, citing Forrester/Akeneo as well as its own project experience ↩
- smartworker – DIHK digitalisation survey 2026: 5,000 companies on AI maturity in the German Mittelstand ↩