Retail — Retail E-Commerce Recommendations Feature Engineering Pipeline

Overview

The primary purpose of this DAG is to facilitate the creation of features that improve the recommendation system for retail e-commerce platforms. It begins by ingesting various customer data sources, including transaction logs, user behavior analytics, and demographic information. The data ingestion pipeline employs normalization techniques to standardize the data, followed by validation processes to ensure accuracy and completeness. Processing steps include feature extraction, where relevant at

The primary purpose of this DAG is to facilitate the creation of features that improve the recommendation system for retail e-commerce platforms. It begins by ingesting various customer data sources, including transaction logs, user behavior analytics, and demographic information. The data ingestion pipeline employs normalization techniques to standardize the data, followed by validation processes to ensure accuracy and completeness. Processing steps include feature extraction, where relevant attributes are derived from raw data, and feature engineering, which involves creating new variables that enhance predictive capabilities. Quality controls are implemented at each stage to monitor data integrity, including checks for consistency and outlier detection. The final outputs are stored in a feature store, enabling easy access for machine learning models. Key performance indicators (KPIs) for this DAG include processing time and feature accuracy, which are critical for assessing the effectiveness of the recommendation system. The business value derived from this DAG lies in its ability to enhance customer engagement through personalized recommendations, ultimately driving sales and improving customer satisfaction.

Part of the Recommendations solution for the Retail industry.

Use cases

• Improves customer engagement through personalized recommendations.
• Enhances marketing strategies with data-driven insights.
• Increases sales by providing relevant product suggestions.
• Boosts customer satisfaction with tailored shopping experiences.
• Reduces data processing time through efficient workflows.