The most challenging aspect for many automotive businesses is the high volume of product data. This challenge quickly becomes more complex when it comes to ensuring the compatibility of automotive products, such as spare parts and accessories, for different vehicles. Traditional manual data input and processing procedures are time-consuming and error-prone, resulting in inconsistent product listings, pricing, and specs. Many businesses must seek new, more robust, and powerful product management solutions.
Therefore, implementing an efficient, scalable, and comprehensible digital product data management process is imperative for innovative automotive companies seeking to stay ahead. However, today, achieving such a data strategy involves a great deal of effort for many companies, as product data is usually not ready for online publication, and sourcing product data is time-consuming and fragmented. In addition, oftentimes suppliers and manufacturers can't provide product data in electronic formats. This is in contrast to the business objectives automotive aftermarket companies want to achieve to remain competitive. For one, the product range available online must be continuously expanded to increase sales. Also, there is a need to speed up time-to-market of new product segments while keeping existing products continuously and reliably up to date. Another lever to increase sales is to achieve higher conversion based on increased product data quality. It is data quality that ensures compareability of highly technical products across various suppliers and manufacturers of spare parts. Therefore, how can a comprehensive digital strategy for the automotive aftermarket achieve these goals?
Knowing that it is challenging to prepare various data types from different sources and enrich missing product data, the AI-powered product data platform can transform product data from various fragmented sources into an import file ready to import to any ERP, PIM or MAM system in use. Trained with over 750 million SKUs and over 1000 suppliers, the Onedot AI is the most advanced product data transformation platform in the market. The platform enables a rapid time-to-market for innovative products, equipment, and machinery while maintaining high product data standards. It's the most sustainable approach to offer a well-curated product assortment online while also reducing time-consuming and error-prone manual product data maintenance. In addition, the premium supplier portal allows manufacturers and suppliers easy access to the platform, enabling catalogue uploads with a single click. This simplifies data sourcing and makes the organization and overview of various data sources much easier, including catalogue version histories.
By leveraging the Onedot product data platform, automotive companies can drastically improve their product data management processes. The ground-breaking Onedot product data platform enables users to upload product catalogues and initiate data onboarding processes with a few clicks.
By implementing a large-scale automated product data onboarding process, automotive companies can ensure that the high volume of product data is always accurate, up-to-date, and of the quality needed for digital business success. The structured and proven onboarding process by Onedot leverages machine learning, NLP and LLM algorithms to turn unstructured and messy product data into e-commerce-ready data. But what does this process look like, and how can companies in the automotive industry make the best use of it?
The structured large-scale product data onboarding process kicks off after the supplier catalogues are uploaded to the platform and cleaned up for onboarding using product matching. This ensures that only the desired products of the correct quality find their way into the desired product database. After ensuring the catalogue is ready for onboarding, the process can be kicked off with the click of a button. The first step is to assign the supplier categories to the target categories. The Onedot AI does this automatically, ensuring the categories are mapped correctly. If a category cannot be assigned, the software suggests options that the data team can check. After mapping the categories, the Onedot AI maps supplier with the target attributes. In the downstream normalisation step, units are standardised, and value lists are filled. Finally, product variants are created based on the variant-forming attributes before an import file is created for the product database. This automated onboarding process transforms large-scale product data into ERP, PIM, or MAM ready import files.
The versatility of the Onedot product data platform can benefit all companies that are confronted with complex product data situations on a daily basis. The Onedot product data platform can quickly prepare catalogs according to common standards and thus prepare AI-supported catalogs for syndication without manual effort. Automotive companies can get started easily and quickly with the AI-powered product data platform by Onedot and transform their product data management today.
