Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns

TL;DR

This study compares logit and probit models using Bayesian methods and LOO-CV to predict bank term deposit subscriptions, emphasizing the importance of model choice in financial marketing strategies.

Contribution

It demonstrates the effectiveness of Bayesian evaluation techniques in selecting between logistic and probit models for imbalanced banking data.

Findings

Logit model outperforms probit in prediction accuracy.

Bayesian methods improve model evaluation reliability.

Model choice significantly impacts marketing decision outcomes.

Abstract

In the highly competitive environment of the banking industry, it is essential to precisely forecast the behavior of customers in order to maximize the effectiveness of marketing initiatives and improve financial consequences. The purpose of this research is to examine the efficacy of logit and probit models in predicting term deposit subscriptions using a Portuguese bank's direct marketing data. There are several demographic, economic, and behavioral characteristics in the dataset that affect the probability of subscribing. To increase model performance and provide an unbiased evaluation, the target variable was balanced, considering the inherent imbalance in the dataset. The two model's prediction abilities were evaluated using Bayesian techniques and Leave-One-Out Cross-Validation (LOO-CV). The logit model performed better than the probit model in handling this classification problem. The results highlight the relevance of model selection when dealing with complicated decision-making processes in the financial services industry and imbalanced datasets. Findings from this study shed light on how banks can optimize their decision-making processes, improve their client segmentation, and boost their marketing campaigns by utilizing machine learning models.