B2B product data is often of poor quality, making it difficult to match products accurately. Some of the most common challenges include:
Attempting to match products manually would not only be time-consuming, but also overlook a significant percentage of probable matches.
Using any available information in the product catalog our ML models are able to predict matching products with a high degree of accuracy. Even if GTINs, UPCs or MPN codes are missing, our algorithms use other product data such as titles, descriptions, and digital assets to arrive at matches. And if titles and other fields are incomplete or missing, we use attributes to zero in in the product.
Categorization mismatch? No problem. Different brands, same product? We have that covered. Product variants? We’ll get all of them. Related products? Yes, those as well.
Our product matching is not limited to comparing a single product. We provide business intelligence that adds a whole extra dimension to your analysis, enabling data-driven strategies around:
