With online sales growing faster than ever, traditional retailers are rightfully investing in omnichannel strategies and redoubling their efforts to meet digitally inclined consumer demands. Offering more detailed product information, images and sometimes even video is one way the establishment can help assure and empower buyers and regain their competitive edge versus pure-play e-tailers.
To enhance online product discovery, retailers must maintain and provide digital images and videos, catalog descriptions, category-specific metadata (such as nutritional information for food products), stock availability, product size ranges, product ratings and reviews, pricing, and promotional information for all physical stock keeping units (SKU). Acquiring this information from suppliers is usually a time-consuming task, requiring various handoffs and significant manual labor.
To address this challenge, we built an intelligent automated system that extracts more searchable retail information from actual product labels. Using computer vision, natural language processing, and machine learning techniques, the system can extract important metadata such as product title, product description, volume/weight, nutritional facts, branded logos, and barcodes.
Test results in our labs show 95% accuracy, which is good news for retailers hoping to get their house in order to better compete with metadata rich online stores. Here’s a look at how our technology works and why it’s important.