Blog
June 15, 2022
With the increasing amount of product data and growing complexity, online retailers are facing unprecedented challenges. In a joint webinar with our implementation partner hmmh AG, we demonstrated how online retailers can consistently solve individual data management challenges with scalable, AI-powered SaaS solutions.
Wall paint “Morgentau”: A shade of gray or more of a grayish beige?
Retailers, suppliers, and manufacturers have something in common. They all have their own individually developed product data model. Suppliers and manufacturers face the challenge of providing their retailers with high-quality product data. Retailers, for their part, must find ways to transfer product data efficiently, quickly, and sustainably into their individually developed product data model. In the example of the wall paint “Morgengrau,” this may mean that the retailer has to ask the supplier for the HEX specifications.
The procurement, creation, and processing of product data is therefore costly, ineffective, and overwhelming for most online retailers. Anyone who invests a lot of time and energy in manual data maintenance will inevitably ask themselves whether there is a data model that requires less maintenance. But how do you find the right product data model, and are standards such as ETIM, ECLASS, or BEGROS the solution?
Whether a company-specific or standardized product classification model is used depends on various factors. Neither classification model can cover every possible scenario. Many companies therefore continue to rely on manual sorting and maintenance processes, which are time-consuming and costly and also have a negative impact on data quality.
Innovative online retailers, on the other hand, are trying to counteract this problem with automation. This enables their own data teams to monitor the entire process instead of keeping them busy with tedious manual data maintenance processes.
Next-level data management: Automated product data processes
Automated product creation follows clearly defined processing steps that run automatically. Onedot’s first intelligent product data platform for sourcing, processing, and distributing product data provides various onboarding modules.
For example, Onedot customers can use AI to extract product attributes, classify different products, and convert attribute values and attribute units to the desired individual target format. Only a clearly structured and automated product data onboarding process is scalable and thus ensures data quality in the long term.
How can we help you?
Would you like to know how product data onboarding can be implemented in your company? Are you wondering whether it is possible to automate data processes? We would be happy to advise you together with our partner hmmh AG. Book a personal demo—we look forward to hearing from you.