Catalog Management, CPG & Retail
See how we addressed an e-commerce platform’s challenge of managing diverse product descriptions by implementing a solution leveraging the GPT 3.5 language model. The transformative approach streamlined model upgrades, reducing maintenance time and significantly cutting down model retraining time from two weeks to two days. Despite a slight dip in accuracy, this resulted in enhanced operational efficiency and substantial cost savings for the client, showcasing the solution’s effectiveness in revolutionizing ecommerce product categorization.
The ecommerce marketplace faced was challenged with managing the variability in product categorization and sub-categorization of descriptions provided by sellers, leading to difficulties in accurate product classification within the marketplace’s taxonomy. Initially reliant on manual tagging, the marketplace later automated the categorization process using a Natural Language Processing (NLP) text classification-based Machine Learning (ML) model. Trained on previously tagged products, the model aimed to accurately classify product categories.
Netscribes conducted an audit of the previous solution, identifying key challenges:
We proposed a transformative approach integrating the GPT 3.5 language model and Few-Shot prompt loading techniques. This solution utilized dynamic taxonomy, product titles, and descriptions to enhance the accuracy of category predictions.
The implemented solution led to:
Integrating GPT and dynamic taxonomy, Netscribes not only streamlined ecommerce product categorization but also drastically reduced model retraining time. The result?
Enhanced operational efficiency and substantial cost savings for the e-commerce stalwart.