Product data quality as a success factor in retail
No convincing shopping experience without clean product data
Whether search, filters or offer communication: the quality of product data is now a decisive factor for digital success in retail. Why product data quality is far more than a technical obligation, and how retailers use it strategically as a success factor, is explained by our Senior Digital Consultant in this interview. The conversation took place as part of the ECC CLUB of IFH Köln.
Why is high product data quality so relevant, especially for retail companies?
All offer communication in retail depends directly on the quality of the product information available and usable. Only good product data makes it possible to create a convincing customer experience: customers find the right products through search, filters and comparison, navigation is intuitive and the presentation in the shop is convincing. This improves the user experience and noticeably lifts the conversion rate.
Good product data also reduces the return rate, because the product actually matches its presentation in the shop. That builds trust in the brand and the retailer.
When you talk about "good product data," what do you mean?
Product data quality is based on a few fundamental criteria that always apply. On top of that come individual requirements depending on the target group and use context, which we define and prioritize together with our customers. Key baseline factors are completeness, correctness and consistency.
Completeness is relative here. It starts with the absolute minimum of information needed to sell a product at all, but the scale for the effort involved is open at the top. Having the most complete product data is a competitive advantage. Even so, you should always weigh cost against return, because the principle of marginal utility applies here too, of course.
Correctness seems simple, but it depends on context. For the shop, a figure in whole meters may be enough, whereas product lifecycle management requires exact millimeters.
For frequently updated products, timeliness and clarity are what count. It has to be clearly recognizable which variant it is exactly. In regulated industries such as food, pharma or textiles, compliance with legal requirements is also mandatory.
The information has to be consistent as well, meaning identical across all channels and touchpoints, from the Instagram reel to the price tag in the store. Otherwise customers lose trust and drop out right before the purchase.
The individual aspects are weighted differently depending on the situation. Sometimes it makes more sense to go online quickly with the basic data and add to it later. In other cases it pays off to go the extra mile from the start, in order to stage products that need explanation or command a high price appropriately.
How can you measure data quality, and how do you improve it concretely?
Data quality is a continuous process, not a checkbox you tick off. Spoiler: there is no such thing as done here.
For around 25 years there have been specialized systems for capturing, enriching and distributing product data centrally across various channels: PIM systems for product information management. When the focus is more on using and measuring the success of the data, we speak of PXM, or product experience management.
The goal is always the same: a central, reliable source for all product information, the single source of truth. PIM and PXM systems actively support this quality process. They automatically check for completeness, both in general and for marketplaces with their own requirements such as Amazon or Zalando, without which products often are not even listed in the first place.
What are the typical challenges?
The biggest challenge lies in the variety of data sources. Information comes from internal systems, from manufacturers, suppliers or external data providers. In many industries, specialized service providers also deliver product data as a service. Each of these sources models the data differently and in a technically different form.
In addition, the effort for maintenance and enrichment is often underestimated. Especially with large or changing assortments, this is not a task you handle on the side. It requires expertise about products, processes and systems, and can therefore only be delegated to working students or temporary staff to a limited extent.
With clear processes, automated support and AI, the effort can be reduced significantly, but not eliminated. That is why it is crucial to bring experienced partners on board, partners who accompany the transition to a new system and make sure the people behind the product data manage the switch without jeopardizing their health or ongoing operations.
That is a lot of topics. Where should you start?
The first step is an honest inventory: how is data quality doing right now? How does the lifecycle of product information run in the company? In doing so, you should definitely bring along the team that works on and with the data every day. After that, you prioritize based on pain points and optimization potential: which problems are most urgent, which improvements deliver quick value?
It is important not to fall into the trap of first creating a fully comprehensive plan. Better to move into action after the first analysis and then keep at it continuously. It is best to start with a few low-hanging fruits, points that can be solved quickly and bring immediate, noticeable improvement. That motivates the team and builds momentum. After all, a continuous improvement process is just beginning here.
Questions about your product data?
Michael is happy to help. Let's talk about your starting point, where your product data quality stands today and where the fastest lever is.
