Retail — Product Data Taxonomy Extraction Pipeline

Overview

The Product Data Taxonomy Extraction Pipeline is designed to improve the search and recommendation processes within the retail industry by extracting relevant product data entities and constructing a comprehensive taxonomy. The pipeline ingests data from various sources, including product catalogs, ERP systems, and customer feedback. Initially, the data is collected and normalized to ensure consistency across different formats. Following this, the processing steps involve entity extraction, wher

The Product Data Taxonomy Extraction Pipeline is designed to improve the search and recommendation processes within the retail industry by extracting relevant product data entities and constructing a comprehensive taxonomy. The pipeline ingests data from various sources, including product catalogs, ERP systems, and customer feedback. Initially, the data is collected and normalized to ensure consistency across different formats. Following this, the processing steps involve entity extraction, where key product attributes are identified and categorized. The taxonomy is built to reflect relationships between products, enhancing the overall understanding of product offerings. Quality control measures are implemented at each stage to validate data accuracy and integrity, ensuring that only high-quality data is integrated into the knowledge management system. The outputs of this DAG include structured product data, a detailed taxonomy, and performance metrics that track the effectiveness of the recommendations generated. Monitoring key performance indicators (KPIs) such as recommendation accuracy and user engagement provides insights into the effectiveness of the taxonomy. The business value derived from this pipeline includes improved customer satisfaction through personalized recommendations, increased sales conversion rates, and enhanced operational efficiency by streamlining product data management.

Part of the Literature Review solution for the Retail industry.

Use cases

• Enhances customer experience with tailored product recommendations
• Increases sales conversion rates through improved search relevance
• Streamlines product data management processes
• Improves operational efficiency with high-quality data integration
• Facilitates better decision-making based on accurate product insights