When the Shopping Cart Lies: Why Poor Product Data Slows Down Your Conversion
Why the Real Conversion Killer Is Not in the Checkout
A customer searches online for a vacuum cleaner. He finds a model that, according to the product description, is suitable for hard floors and carpets. He orders it. The device arrives. He reads in the operating manual that it is expressly not recommended for certain types of carpets. He sends it back.
This is not a logistics problem. It is not a UX problem either. It is a data problem.
When people in e-commerce talk about declining conversion rates, they immediately think of checkout optimization, page load times, or the next A/B testing experiment. Yet the real conversion killer often sits much earlier in the customer journey: in the product data itself. Inaccurate descriptions, missing attributes, and contradictory information across channels all erode trust, cost revenue, and ultimately drive away loyal customers.
What Poor Product Data Really Costs in E-Commerce
The figures are clear. According to the current EHI study "Shipping, Packaging, and Returns Management in E-Commerce 2025," 75.2 percent of the online retailers surveyed cite detailed product information and descriptions as the most important measure for reducing returns, ahead of high-quality product images (62.8%) or quality assurance before shipping (47.9%). The logical conclusion: businesses with poor product data are actively generating returns.
The cost becomes apparent when you look at the numbers. According to EHI, one in eight retailers in the German online fashion sector sees return rates above 50 percent, and returns researcher Björn Asdecker estimates that around 550 million parcels are sent back in Germany each year (EHI, 2025).
But the costs of poor data quality extend far beyond returns. Gartner’s Data Quality Market Survey estimated that inaccurate data costs companies an average of 15 million US dollars per year. Experts such as Thomas C. Redman (Harvard Business Review) estimate that the downstream costs of poor data quality can amount to 15 to 25 percent of company revenue, through rework, lost sales, customer churn, and inefficient processes.
Where Data Errors Arise in Multichannel Commerce
Data errors arise wherever product data is not maintained centrally but is spread across systems, teams, and processes.
In many companies, product data is maintained simultaneously in the ERP system, supplemented in spreadsheets, adjusted in the shop backend, and manually reformatted for each marketplace. Every new channel increases the probability that information diverges. Product variants are described inconsistently. Images no longer match the current product generation. Measurements fluctuate between centimeters and inches depending on which system last modified the record.
The result: the same product has different specifications on the company’s own website than on Amazon, different images than on Otto, and a different description than in the print catalog. Customers become confused. Companies incur liability.
The situation becomes especially critical as the number of channels grows. According to the HDE Online Monitor 2024, 54 percent of German e-commerce revenue is processed through marketplaces. Each additional marketplace multiplies the maintenance effort and the risk of inconsistent data.
Why AI First Commerce Fails Without Clean Product Data
At the K5 Future Retail Conference, the most important industry gathering in German e-commerce, topics such as AI First Commerce, hyper-personalization, and retail media have dominated the agenda for two years. These are the right conversations. But they all rest on a premise that is rarely stated explicitly: that the underlying product data is reliable, complete, and consistent.
Businesses that roll out AI-powered product recommendations while working with inconsistent master data are only accelerating flawed targeting across more channels at once. Those who invest in retail media but operate product pages with incomplete information are paying for traffic that does not convert. And anyone who promises hyper-personalization without knowing which attributes belong to which product will ultimately deliver personalized data noise.
The foundation for all these future investments is clean, centralized, and consistent product data. Without this foundation, AI First Commerce remains a compelling keynote thesis and nothing more.
How a PIM System Solves the Product Data Problem
The good news is that the problem is solvable. And it does not require a complete reinvention of existing processes, but above all one thing: a central location where product data is maintained, enriched, and distributed to all channels from there.
The principle is called Single Source of Truth: a single, authoritative source for all product-relevant information. Changes are made once and flow automatically and consistently into all connected systems: the company’s own shop, marketplaces, print media, and B2B portals. No manual duplication of effort. No version conflicts between ERP and shop backend.
A Product Information Management (PIM) system is the technological foundation for this approach. Centrally maintained, complete, and consistent product data reduces manual overhead, accelerates time-to-market for new products, and has a direct impact on return rates and therefore on margins.
NovaDB goes a step further than traditional PIM systems: as an all-in-one content cloud, the platform combines product data management, content management, and digital asset management on a single headless, API-first architecture. This means no data silos between PIM, DAM, and CMS, but a continuous flow of information from the data record to channel delivery, in real time, in every language, for every channel.
Where Real Conversion Optimization Begins
No A/B test and no checkout redesign will win back customers who abandoned their purchase or returned a package because of inaccurate product information. Conversion optimization that addresses the surface while ignoring the underlying data problem is costly and ineffective.
The real question is: do you know how many of your returns are caused by inaccurate or inconsistent product data?
If you cannot answer that question with confidence, that is already an answer.
The most common cause of returns is inaccurate or incomplete product descriptions. According to the EHI study 2025, 75.2 percent of online retailers cite detailed product information as the most important measure for preventing returns. Further causes include incorrect measurements, contradictory specifications across different channels, and missing product images.
Single Source of Truth refers to a central data repository from which all sales channels are automatically and consistently supplied with product information. Changes are made once and flow into shop, marketplaces, print, and B2B portals without manual rework. The opposite is decentralized data maintenance across ERP, spreadsheets, and shop backend, where discrepancies inevitably arise.
A PIM system pays off as soon as a company serves more than one sales channel, manages more than a few hundred products, or must maintain product data in multiple languages. The need increases significantly when product variants, marketplace-specific data formats, or frequent assortment changes make the manual effort in existing systems unmanageable.
An e-commerce platform specializes in transaction processing: shopping cart, checkout, payment, and order management. A PIM system handles the upstream task: centrally capturing, enriching, quality-assuring, and channel-specifically distributing product data. Both systems complement each other. A PIM does not replace an e-commerce platform but makes it more powerful.
Product data is the most important basis for decision-making in online purchasing. Incomplete or inconsistent product data leads directly to purchase abandonment, returns, and loss of trust, resulting in measurable conversion losses.
Tobias Denninger
After completing his Bachelor of Science in Online Marketing at the University of Applied Sciences Würzburg-Schweinfurt (THWS) in 2021, he began his career in sales at Noxum. There, he gained solid experience in business development and continuously expanded his expertise.
In 2022, he took on the role of Account Manager with a focus on NovaDB. In this position, he is responsible for developing customer relationships and delivering tailored solutions based on NovaDB technology, with the aim of building long-term partnerships and creating measurable value for clients.