Bank Marketing Campaign Response Prediction in Digital ERA

DOI

10.2991/978-94-6239-674-6_32How to use a DOI?

Keywords

Bank promotional campaigns In Digital ERA; Sustainability; Precision; F1- Score; imbal-anced classification; key metrics

Abstract

In the age of digital, bank sector campaigns are often challenged by poor customer engagement causing significant waste in re- sources. Marketing dollars are often being wasted because potential sub- scribers aren’t being identified properly. This research aims to deliver a structured prediction model that solves the above challenge with an enhanced accuracy rate through advanced data exploration, pre-processing and machine learning methodology. This methodology combines (1) rigorous data treatment, handling missing values and advanced feature engineering, focusing on comprehensive Bayesian hyperparameter tuning with ensemble learning to improve predictive accuracy. EDA showed a severe class imbalance, where a much larger share are non-subscribers. Of the trained models, models with the more advanced algorithms achieved high precision and F1-score (which is relevant for imbalanced classification). The suggested approach led to significant gains in campaign conversion, and decreases in marketing budget, at 35–45% higher conversion (driving a 20–25% savings). These findings offer a substantive and empirical best practice for enhancing data-driven bank marketing in the digital era.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Harsh Mishra
AU  - Shilpi Yadav
AU  - Shobit Agrawal
AU  - Dibyanarayan Hazra
AU  - Nandini Gupta
AU  - Manish Raj
PY  - 2026
DA  - 2026/05/28
TI  - Bank Marketing Campaign Response Prediction in Digital ERA
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 377
EP  - 390
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_32
DO  - 10.2991/978-94-6239-674-6_32
ID  - Mishra2026
ER  -