Blog
April 26, 2024
Retailers, suppliers, manufacturers, and brands face major challenges in data management: large amounts of data must be structured, processed, and made available to end customers in high quality for sale. Unfortunately, product information is still very often prepared 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 be achieved? And how can data management processes be significantly improved through the use of artificial intelligence?
AI-supported product data processes are key to competitiveness because they enable significantly faster onboarding of supplier catalogs, improved product data quality, and faster time-to-market for products for sale. The targeted and rapid expansion of the online product range is also possible with the thoughtful use of artificial intelligence in product data onboarding. However, all this can only be achieved with well-thought-out product data management that can provide product data quickly, efficiently, and correctly for display on a wide variety of channels.
Completeness, availability, and consistency of product data centrally
In principle, a company uses different systems such as ERP, PIM, and DAM to collect, store, manage, and share data across different departments. 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 contrasts with 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 high e-commerce quality. Further 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 filled to the highest possible degree.
Today, online shops quickly accumulate over 50,000 products that need to be managed. However, these cannot be managed without considerable manual effort if they are not bundled together in a single system with the same level of granularity and consistency. This is another reason why many companies use a PIM system. It allows product data to be stored systematically in the same way. This is exactly where the Onedot product data platform comes in: all upstream, sometimes very time-consuming manual preparation processes are structured and AI-supported, and converted into an importable PIM or ERP format. In addition to the onboarding of supplier or manufacturer catalogs, these time- and cost-intensive preparation processes also include data acquisition. The clearly structured onboarding process via the Onedot product data platform can help here. The Starter Packages offer a simple, fast, and intuitive introduction to the Onedot software. These are designed to enable companies to quickly determine their business added value and get started with Onedot AI.
Cost reduction and efficiency thanks to automated product data onboarding
The main goal of a robust, AI-supported onboarding process must be to prepare product information from manufacturers and suppliers in as automated a manner as possible so that it has a structure that either complies with one of the common standards or with the retailer’s structure. Onedot AI can import and map 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. In addition, the Onedot product data platform features a clear and collaboration-friendly supplier portal. This greatly simplifies communication and exchange between retailers and suppliers via a chat function, eliminating the need to send countless emails back and forth across multiple versions. Inquiries can be handled quickly via the platform, and suppliers can be authorized to initiate onboarding themselves. This not only has a positive effect on data acquisition, but also automates the preparatory work for onboarding product data.
An automated onboarding process with Onedot AI essentially always includes the following steps: Once the product data comparison 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 the 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. After that, units and value lists are filled uniformly in the normalization step. There are other ways to profitably use the powerful Onedot AI, which has now been trained with over 750 million SKUs from over 1,000 suppliers: The attribute extraction capability of Onedot AI is unbeatably good and can extract product information from continuous text in a structured manner for the desired target system and, in the event of deviations, leave attributes empty or reformulate them accordingly. 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 even for non-technical users to navigate 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 maintaining accurate product data. The high degree of automation in data preparation and maintenance processes allows companies to update their product information in real time, so customers always have access to the latest and most accurate details. This agility is crucial in a dynamic e-commerce landscape.