Extracting Features from Marketing Data

This article presents a structured pipeline for transforming raw marketing data into a modelling-ready dataset by engineering features from customer interaction logs, promotional event records, and transactional histories. Through systematic preprocessing, aggregation, and merging of engagement (fe_), event (ev_), and transaction (tr_) datasets, the final output enables robust machine learning applications. The resulting dataset supports classification, clustering, anomaly detection, and segmentation tasks, offering a scalable foundation for predictive analytics and strategic customer insights in marketing contexts.