Transport & Logistics — Feature Engineering for Propensity Scoring

Overview

The 'Feature Engineering for Propensity Scoring' DAG is designed to extract and create significant features from normalized datasets, aimed at improving the accuracy of propensity scoring models. The primary purpose of this DAG is to facilitate customer personalization by leveraging data-driven insights. The data sources include transaction logs, customer profiles, and operational metrics, which are ingested into the system for processing. The ingestion pipeline is structured to ensure seamless

The 'Feature Engineering for Propensity Scoring' DAG is designed to extract and create significant features from normalized datasets, aimed at improving the accuracy of propensity scoring models. The primary purpose of this DAG is to facilitate customer personalization by leveraging data-driven insights. The data sources include transaction logs, customer profiles, and operational metrics, which are ingested into the system for processing. The ingestion pipeline is structured to ensure seamless data flow and integrity. The processing steps involve several key operations: first, data analysis is conducted to identify potential features; next, new variables are created based on existing data; then, feature validation is performed to ensure the quality and relevance of the features generated. Quality controls are integrated at each step to monitor the accuracy of the features, with specific KPIs including feature precision and processing time. The final outputs are stored in a feature store, allowing easy access for data science teams to utilize these features in their models. This DAG not only streamlines the feature engineering process but also enhances the overall efficiency of customer personalization efforts within the transport and logistics industry. By providing high-quality, validated features, businesses can significantly improve their scoring models, leading to better customer targeting and engagement.

Part of the Customer Personalization solution for the Transport & Logistics industry.

Use cases

• Improves customer targeting through accurate propensity scoring
• Enhances data-driven decision-making in logistics operations
• Reduces time spent on feature engineering tasks
• Increases model accuracy with high-quality features
• Enables rapid access to features for data science teams