SKU Validation: A vital step towards product data quality

Product information gets accumulated in a legacy system that has been in use for decades. Many products are given internal codes that are not aligned with industry standards. Differences in codes would mean that customers as well as internal teams might not be able to access these products, and their sales and trade might get impacted severely, all of which points to a major loss of revenue. Additionally, lack of standard product codes makes it difficult to automate any kind of enrichment, analysis and processing of this data.

With an effectively automated validation of SKUs , such problems can be solved. Our process matches products accurately through advanced algorithms. These algorithms would identify product data such as descriptions, titles, specifications, and more, and perform accurate product matching. 

In the case where larger descriptions and specifications do not help with accurate matching, these algorithms are also capable of matching products based on their attributes. This would also ensure precise coding of data into GTIN/MPN, etc.

This process also allows for the data to be structured, since it also includes UPC codes, and manufacturer and brand names, all presented in an organized manner. 

• Intelligent Automation: Our models are pre-trained on product data, which enables intelligent automation right from the start, without the need for extensive set up. 
• Time Efficiency: We reduce dependency on purely manual processes by largely automating the validation and standardization processes, significantly saving effort and time.
• Cost Reduction: Apart from the obvious savings with reduced dependencies, a hidden cost associated with lost sales can be avoided as product accuracy increases–costly returns, supply chain inefficiencies and product visibility can have significant impact on the bottomline.  
• Human-in-the-Loop: We maintain high levels of accuracy by allowing for human intervention for edge cases or when specific changes are required. These corrections are then fed back into the algorithm, which ensures a better performance with every run.