Nikita Katyal

This research work tackles the challenge of accurately categorizing customers for retailers to enable precise, targeted marketing strategies. It introduces an innovative method leveraging Large Language Models (LLMs) to analyze purchase data and identify key product categories, such as baby food or eldercare items, revealing household compositions like families with kids or elders. By integrating historical behavior with LLM-based classification, the approach enhances life stage segmentation, improving customer targeting, sales, and retention. The research paper details the data sources, model architecture, and evaluation metrics used in this segmentation.