Retail — User Behavior and Product Feature Engineering Pipeline

Overview

The primary purpose of this DAG is to improve product recommendations in the retail sector by engineering relevant features from user behavior and product data. The data sources include user interaction logs, product catalogs, and historical sales data, which are ingested through a structured pipeline. The ingestion process begins with data extraction from multiple sources, followed by data cleansing to remove inconsistencies and errors. Next, feature engineering techniques are applied to derive

The primary purpose of this DAG is to improve product recommendations in the retail sector by engineering relevant features from user behavior and product data. The data sources include user interaction logs, product catalogs, and historical sales data, which are ingested through a structured pipeline. The ingestion process begins with data extraction from multiple sources, followed by data cleansing to remove inconsistencies and errors. Next, feature engineering techniques are applied to derive meaningful attributes from the raw data, such as user preferences, product popularity, and seasonal trends. Validation steps are incorporated to ensure that the features created meet quality standards, which involves statistical analysis and comparison against historical performance. The final outputs of the DAG include a refined set of features that are fed into a scoring system, allowing for real-time adjustments to recommendations based on user interactions. Key performance indicators (KPIs) such as feature importance scores, recommendation accuracy, and user engagement metrics are monitored to evaluate the effectiveness of the features. The business value of this DAG lies in its ability to enhance the personalization of product recommendations, leading to increased customer satisfaction and higher conversion rates.

Part of the Literature Review solution for the Retail industry.

Use cases

• Increased accuracy of product recommendations
• Enhanced user engagement through personalized experiences
• Improved conversion rates from targeted recommendations
• Data-driven insights for strategic decision-making
• Scalable solution to accommodate growing data volumes