Retail — E-Commerce Feature Engineering Pipeline for Predictive Analytics

Overview

The primary purpose of this DAG is to create a robust feature engineering pipeline that leverages sales and customer behavior data to support predictive analytics in the retail sector. The pipeline ingests diverse data sources, including transaction logs, customer interaction records, and inventory data. Initially, data is collected from ERP systems and CRM platforms, ensuring comprehensive coverage of customer touchpoints. The ingestion pipeline is designed to handle large volumes of data effic

The primary purpose of this DAG is to create a robust feature engineering pipeline that leverages sales and customer behavior data to support predictive analytics in the retail sector. The pipeline ingests diverse data sources, including transaction logs, customer interaction records, and inventory data. Initially, data is collected from ERP systems and CRM platforms, ensuring comprehensive coverage of customer touchpoints. The ingestion pipeline is designed to handle large volumes of data efficiently, transforming raw data into structured formats suitable for analysis. Processing steps include data cleaning to remove inconsistencies, feature extraction to identify relevant metrics such as purchase frequency and average transaction value, and feature transformation to normalize and scale the data for model readiness. Quality control measures are implemented at each stage, including validation checks to ensure data integrity and performance monitoring to evaluate the effectiveness of predictive models. Key performance indicators (KPIs) such as model accuracy, precision, and recall are tracked to ensure continuous improvement. The outputs of this DAG include a set of engineered features ready for model training, performance reports detailing model efficacy, and dashboards for real-time monitoring of predictive analytics. By automating the feature engineering process, this DAG provides significant business value, enabling retailers to make data-driven decisions that enhance customer engagement and optimize inventory management.

Part of the Document Automation solution for the Retail industry.

Use cases

• Improves accuracy of predictive models for sales forecasting
• Enhances customer targeting through refined analytics
• Reduces manual effort in feature engineering processes
• Increases operational efficiency by automating data workflows
• Enables timely insights for strategic decision-making