Work

Extracting Features from Marketing Data

Year: 2025

Type: article

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16134232

Author: Majumdar, Partha

Language: English

Topic: Customer churn and segmentation

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W6949540253

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