Retail — E-Commerce Customer Behavior Feature Extraction

Overview

The primary purpose of the 'E-Commerce Customer Behavior Feature Extraction' DAG is to extract and prepare features from customer and transaction datasets for advanced analytics in the retail sector. The data sources include customer profiles, transaction logs, and product catalogs, which are ingested into the pipeline for processing. The architecture consists of several key steps: first, data is ingested from specified sources, followed by transformation processes that include normalization and

The primary purpose of the 'E-Commerce Customer Behavior Feature Extraction' DAG is to extract and prepare features from customer and transaction datasets for advanced analytics in the retail sector. The data sources include customer profiles, transaction logs, and product catalogs, which are ingested into the pipeline for processing. The architecture consists of several key steps: first, data is ingested from specified sources, followed by transformation processes that include normalization and feature extraction techniques. Quality controls are implemented to ensure the accuracy and relevance of the features extracted, with monitoring mechanisms in place to track extraction time and feature precision as key performance indicators (KPIs). The final outputs are stored in a feature catalog, which can be utilized in predictive models to enhance decision-making processes. In the event of a failure, the DAG is designed to automatically restart the extraction process after notifying relevant stakeholders. This approach not only streamlines the feature extraction process but also ensures compliance with governance standards, ultimately driving business value through improved insights into customer behavior.

Part of the Governance & Compliance solution for the Retail industry.

Use cases

• Improved insights into customer purchasing patterns
• Enhanced predictive modeling capabilities for better decision-making
• Streamlined compliance with governance standards
• Increased operational efficiency through automation
• Reduced time to market for analytical models