Retailers, suppliers, manufacturers and brands face major challenges in data management: large volumes of data need to be structured, processed and made available to end customers in high quality for sale. Unfortunately, the preparation of product information is often done manually, which is very time-consuming and, above all, cost-intensive. So how can the process of transforming raw supplier data into a realistic shopping experience succeed? And how can data management processes be significantly improved thanks to the use of artificial intelligence?
AI-supported product data processes are key to competitiveness, as they enable significantly faster onboarding of supplier catalogs, improved product data quality and a faster time-to-market of products for sale. The targeted and rapid expansion of the online range is also possible with the well thought-out use of artificial intelligence in product data onboarding. However, all of this can only be achieved with well thought-out product data management that can provide product data quickly, efficiently and correctly for playout on a wide variety of channels.
In principle, a company has different systems such as ERP, PIM and DAM for recording, storing, managing and sharing data across different areas of the company. A robust and future-oriented IT infrastructure facilitates the transfer of information within the organization. In many companies, however, these data processes are still largely manual.
This is in contrast to the fact that these processes need to be digitized and automated as far as possible, especially in the area of product data. There are various reasons for this: If companies want to sell their own products or products from manufacturers and suppliers online, these products must be available in a high e-commerce quality. Other challenges include the gradual expansion of the online product range and the use of various online and offline sales channels. In terms of product data quality, this means that product data should be complete and as comprehensive as possible, i.e. the attributes should be as full as possible.
Today, online stores quickly have over 50,000 products that need to be managed. However, these cannot be managed without considerable manual effort if they are not available in their entirety in the same granularity and consistency in one system. This is another reason why many companies use a PIM system for this purpose. This allows product data to be systematically stored in the same way. And this is exactly where the Onedot product data platform comes in: All upstream, sometimes very time-consuming manual preparation processes are prepared in a structured and AI-supported manner and converted into an importable PIM or ERP format. In addition to the onboarding of supplier or manufacturer catalogs, these time-consuming and cost-intensive preparation processes also include data procurement. This is where the clearly structured onboarding process via the Onedot product data platform can help. The Starter Packages offer a simple, quick and intuitive introduction to the Onedot software. These are designed to help companies quickly determine their business added value and get started with Onedot AI.
The main goal of a robust and AI-supported onboarding process must be that the product information from manufacturers and suppliers can be prepared as automatically as possible in such a way that it has a structure that corresponds either to one of the common standards or to the retailer structure. The Onedot AI can import, map or generate standards such as ECLASS, ETIM or UNSPSC as well as various versions of BMEcat in versions 1.1, 1.2 and 2005, JSON, Excel or CSV. The Onedot product data platform is also characterized by its clear and collaboration-friendly supplier portal. This significantly simplifies communication and exchange between retailers and suppliers using the chat function, meaning that countless emails no longer need to be sent back and forth across multiple versions. Requests can be processed quickly via the platform and suppliers can be authorized to initiate onboardings themselves. This not only has a positive effect on data procurement, but preparatory work for the onboarding of product data can also be automated.
An automated onboarding process with Onedot AI essentially always includes the following steps: After product data matching has been completed and the products relevant for onboarding have been selected, Onedot's trained artificial intelligence takes over the categorization, mapping and normalization of attribute values. This means that the Onedot software suggests how the supplier products can be mapped to the category tree of the target system and then to category-specific attribute names. Units and value lists are then filled uniformly in the normalization step. There are further possibilities to utilize the powerful Onedot AI, which has been trained with over 750 million SKUs from over 1000 suppliers: The attribute extraction capability of the Onedot AI is unbeatable and can extract product information from continuous text in a structured way to the desired target system and leave attributes blank or reformulate them accordingly in the event of deviations. It is also possible to create golden records, both of which are important options for automated data enrichment.
The unique self-service approach enables companies to manage their product data onboarding independently, significantly reducing the time and costs associated with manual processes. Onedot's intuitive user interface makes it easy for even non-technical users to find their way around the platform and use it effectively. In addition, the self-service approach democratizes the data collection process and ensures that teams from different departments can contribute to the maintenance of accurate product data. The high level of automation in the data preparation and maintenance processes allows companies to update their product information in real time so that customers always have access to the latest and most accurate details. This agility is crucial in a dynamic e-commerce landscape.
