Advice and sales in the bike sector are moving into the digital world. To ensure that the change works for both retailers and producers, uniform standards are needed for product data. However, its value is still underestimated.

For Internet traders like Rose, which with its 15 million visitors per year now is one of the most frequented online bike shops in Germany, data is all-important. Unlike the trained staff behind a store counter, an online shop outlet actually has no direct physical contact with its customers. Whether an initial purchase comes from a complete novice or an expert is not something that a system can determine from just mouse clicks. However, customers want the right product – otherwise they won’t come back. And just as in a brick-and-mortar shop, the digital salesperson only gets to know customers once they have made several visits and left a lot of data behind. With help from a customer profile, finding the right product then takes just a few clicks. Yet collecting data about shoppers is only one stage on the route to digitisation of the bike market. The difficulty Rose current faces is to link selling its own brands with manufacturer brands in a way that creates a common foundation for data and allows customers to compare easily. Only then will customers be able to decide whether to opt, say, for a lower price, or a higher-spec. The response from Sebastian Bomm of Rose Digital is clear:

The route to the perfect products calls for proper data curation

This desired standardisation process is still currently eating up a lot of resources but is an important cornerstone for successful digitisation.


Alex Thusbass is similarly aware of this issue. He is one of the brains behind the BikeCenter, a digital PoS tool for specialist retailers. For him comparable product data provides the foundation, but its importance has yet to be universally recognised. His concern is that “Everybody has product data, everybody needs it, but nobody takes it seriously.” In his view, much of the data provided by manufacturers is poorly processed, not comparable and by no means transparent. The digital expert quotes colours as an example. The potential customer may be looking for a blue bike, whereas many product flyers fail to mention the colour entirely – using instead trendy terms such as Azure, Ocean or Indigo. This may well be legitimate, he claims, but it is not good for a comparative database.

Anybody without inside knowledge has no idea what some details might mean. We need standards so that data can be transferred into the 21st century. If everybody has a different way of spelling Shimano in their datasheets, this does not make the product any better

Manufacturers supply their own ideas and concepts, leaving the retailer to take care of data curation. As a result, producers and retailers incur tremendous additional costs every year because extra staff are needed for standardisation.


Yet the technical facilities to carry out a basic standardisation process already exist. Thusbass raises the subject of a PIM system. The abbreviation stands for Product Information Management, which provides a means of digitally managing and unifying product data so that they can be prepared in standardised form for the websites, online shops and apps but also catalogues, flyers and databases. The latter application allows firms to link up with each other and exchange standardised data. Take this example: until now, data managers at individual bike manufacturers have entered component data into their own systems, all with their own variant spellings and standards. In contrast, an interconnected system will allow component producers to enter the data directly. Any changes a producer introduces to a component name, for example, will then be automatically adapted into all the interlinked product datasheets, across all brands. Automatic references to service videos, product texts and the like can also be included. Data expert Thusbass estimates that automation could lead to a time saving of roughly 40 per cent and a substantial reduction in concealed costs for data management.

We have to create an industry solution for data analysis. It will bring benefits for both industry and retailers