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Machine Learning Insights: Identifying Factors for Successful Bank Telemarketing Campaigns
- 1 Gannon University
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Abstract
Bank telemarketing campaigns play a pivotal in fostering customer relationships and promoting financial products. However, the factors that contribute to the success of these campaigns are multifaceted and often elusive. This study utilizes a range of machine learning techniques to analyze an extensive dataset of telemarketing campaigns from a Portuguese banking institution, shedding lights on critical determinants of its success. The data underwent the application of several machine learning algorithms, including Decision Trees, Random Forest, Logistic Regression, Gradient Boosting, and Naïve Bayes, facilitating the discovery of notable patterns and correlations. Findings revealed that variables such as age, occupation, seasonality, and the number of phone calls exert significant influence on campaign outcomes. By leveraging these insights, banking institutions and marketing strategists can craft more effective, data-driven telemarketing strategies. This in turn stands to enhance marketing efficacy, customer acquisition, and retention, translating into improved business performance.
Keywords
machine learning, bank telemarketing, marketing strategists
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Cite this article
Guan,Y. (2023). Machine Learning Insights: Identifying Factors for Successful Bank Telemarketing Campaigns. Advances in Economics, Management and Political Sciences,43,40-50.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 7th International Conference on Economic Management and Green Development
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