Retail — Knowledge Extraction from Unstructured Retail Data

Overview

The primary purpose of this DAG is to automate the extraction of relevant information from unstructured data sources within the retail sector, such as customer reviews and blog articles. By utilizing advanced natural language processing techniques, the DAG identifies key trends and insights that can inform business strategies. The architecture consists of an ingestion pipeline that collects data from various sources, including social media, customer feedback platforms, and e-commerce websites. T

The primary purpose of this DAG is to automate the extraction of relevant information from unstructured data sources within the retail sector, such as customer reviews and blog articles. By utilizing advanced natural language processing techniques, the DAG identifies key trends and insights that can inform business strategies. The architecture consists of an ingestion pipeline that collects data from various sources, including social media, customer feedback platforms, and e-commerce websites. The processing steps involve text preprocessing, sentiment analysis, entity recognition, trend identification, and storage in a knowledge graph for future reference. Quality controls are implemented at each stage to ensure the accuracy and relevance of the extracted insights. The outputs include a structured knowledge graph and a summary report detailing the insights obtained. Monitoring KPIs, such as the number of insights extracted and extraction time, are tracked to assess the efficiency and effectiveness of the DAG. This solution provides significant business value by enabling retailers to better understand customer sentiments, improve product offerings, and enhance marketing strategies.

Part of the Scientific ML & Discovery solution for the Retail industry.

Use cases

• Enhances decision-making with data-driven insights
• Improves customer engagement through better understanding
• Optimizes product offerings based on customer feedback
• Increases operational efficiency with automated processes
• Supports targeted marketing strategies using extracted trends