With the increasing amount of product data and growing complexity, online retailers are challenged today like never before. In the joint webinar with our implementation partner hmmh AG, we were able to show how online retailers can consistently solve individual challenges in data management with scalable and AI-supported SaaS solutions.
Retailers, suppliers and manufacturers have something in common. They all have an individually developed product data model. Suppliers and manufacturers face the challenge of providing their retailers with high-quality product data. For their part, retailers must find ways to transfer product data efficiently, quickly and sustainably into their individually developed product data model. In the example of wall paint "Morgengrau", this can mean that the retailer has to ask the supplier for the HEX data.
The procurement, creation and preparation of product data is therefore cost-intensive, ineffective and overwhelming for most online retailers. Those who invest a lot of time and energy in manual data maintenance are therefore inevitably faced with the question of 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 perhaps the solution?
Whether to rely on a company-specific or a standardised product classification model depends on various factors. For both classification models it is true that not every case can be covered. Many companies therefore still rely on manual sorting and maintenance processes, which are time-consuming and cost-intensive and also have a negative impact on data quality.
Innovative online retailers, on the other hand, are trying to counter this problem with automation. They are empowering their own data teams to oversee the overall process instead of burdening these teams with tedious manual data maintenance processes.
The automated article creation runs along clearly defined preparation steps, which run automatically. The first intelligent product data platform for the procurement, preparation and distribution of product data from Onedot provides various onboarding modules.
For example, Onedot customers can extract product attributes with AI support, classify different products, and bring 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 sustainable data quality.
Would you like to know how product data onboarding can be implemented in your company? Are you wondering whether the automation of data processes is possible? We would be happy to advise you together with our partner hmmh AG. Book a personal demo - we look forward to hearing from you.