Version 1
: Received: 30 August 2024 / Approved: 30 August 2024 / Online: 2 September 2024 (12:03:31 CEST)
How to cite:
El-Hajj, M.; Pavlova, M. Predictive Modelling of Customer Response to Marketing Campaigns. Preprints2024, 2024090001. https://doi.org/10.20944/preprints202409.0001.v1
El-Hajj, M.; Pavlova, M. Predictive Modelling of Customer Response to Marketing Campaigns. Preprints 2024, 2024090001. https://doi.org/10.20944/preprints202409.0001.v1
El-Hajj, M.; Pavlova, M. Predictive Modelling of Customer Response to Marketing Campaigns. Preprints2024, 2024090001. https://doi.org/10.20944/preprints202409.0001.v1
APA Style
El-Hajj, M., & Pavlova, M. (2024). Predictive Modelling of Customer Response to Marketing Campaigns. Preprints. https://doi.org/10.20944/preprints202409.0001.v1
Chicago/Turabian Style
El-Hajj, M. and Miglena Pavlova. 2024 "Predictive Modelling of Customer Response to Marketing Campaigns" Preprints. https://doi.org/10.20944/preprints202409.0001.v1
Abstract
In today’s data-driven marketing landscape, accurately predicting customer responses to marketing campaigns is critical for optimizing engagement and return on investment (ROI). This study utilizes a Decision Tree (DT) model to identify key factors influencing customer behaviour. Initially, the model achieved a high accuracy of 87.3% but struggled with precision and recall due to class imbalance. By applying a resampling technique, the model’s performance improved significantly, with a recall increase from 44% to 83.1% and an F1-score improvement from 49% to 74.2%. Key influential features identified include how recently a customer made a purchase, the number of days they have been a customer, and the number of previous campaigns they responded to. The study highlights the DT model’s interpretability, making it a practical tool for marketing professionals to improve campaign effectiveness and customer targeting.
Keywords
Customer Relationship Management; Customer response prediction; Decision Tree model; F1-score; ROI optimization; Predictive modeling
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
