{"id":18935,"date":"2024-11-15T17:14:07","date_gmt":"2024-11-15T16:14:07","guid":{"rendered":"https:\/\/www.datasolution.fr\/?p=18935"},"modified":"2026-01-22T12:37:11","modified_gmt":"2026-01-22T11:37:11","slug":"ai-product-data","status":"publish","type":"post","link":"https:\/\/www.datasolution.fr\/en\/ai-product-data\/","title":{"rendered":"AI serving product experience"},"content":{"rendered":"\r\n
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\u2026and thus, supporting product data, which is often stored, qualified, and contextualized within a Product Data Repository like a PIM before being distributed across various channels (stores, e-commerce sites<\/a>, apps) or broadcasted (print catalogs, displays\u2026).<\/p>\n <\/div>\n <\/div>\n <\/div>\n\n\n\n\r\n

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AI, through third-party solutions, specific developments, or direct PIM integration, plays a key role in structuring data. It enables the enrichment, classification, translation, and refinement of product data.<\/p>\n

Concrete examples of AI applications:<\/strong><\/p>\n

At “Foussier<\/a>“,<\/strong> a national distributor of leading building hardware, power tools, and PPE brands for building finishing, a ChatGPT-based tool was implemented to structure data extracted from sparse supplier files, followed by marketing enrichment using product data from manufacturers, scraped from the web.<\/p>\n

The expected benefits included improved product data quality and real-time control over imported data.<\/p>\n

Another use case, presented at the HubForum, involved “Cdiscount<\/a>” to enhance product listing categorization. By 2022, Cdiscount\u2019s Data Science team had already developed algorithms leveraging vast data volumes: visitor numbers (+16 million) and product numbers (+80 million). With the advent of generative AI, Cdiscount\u2019s DATA team accelerated the categorization process, handling 14 million product listings out of 35 million submitted to the system. The business impact was immediate, with a 30% increase in conversions<\/a> on products that benefited from enhanced categorization, thanks to improved information quality.<\/p>\n

Another example involved a Proof of Concept (POC)<\/strong> with a DAM<\/a> system to identify and sort images from zip files and organize them into folders and subfolders. This effort sorted 3,300 images with an accuracy rate of 92-95%. AI tools from Google, such as Google Lab, Keras, and TensorFlow, were used to complete this POC on a fashion image database, with a keyword configuration file established for search functionality and the creation of directories and subdirectories to organize the data.<\/p>\n <\/div>\n <\/div>\n <\/div>\n\n\n\n\r\n

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DATASOLUTION\u2019s PIM\/MDM<\/a> clients generally use AI in two phases :<\/p>\n