Improving Targeted Client Acquisition : Predictive Analysis of Retail Bank Direct Marketing Campaigns

Abstract

In today's competitive banking landscape, effective client acquisition strategies are essential for sustaining growth and profitability. Direct marketing campaigns serve as a primary tool for targeting specific client segments and promoting financial products and services. However, the success of these campaigns’ hinges on the ability to accurately predict client responses and optimize resource allocation. In this paper, we present a predictive analysis framework for improving targeted client acquisition in retail bank direct marketing campaigns. Utilizing phone-based campaigns and client subscription outcomes, we employ machine learning techniques to develop predictive models. By leveraging predictive insights, we can optimize client acquisition efforts, increase subscription rates, and achieve greater returns on marketing investments. This paper contributes to the growing body of literature on data-driven marketing strategies in the banking sector and offers actionable insights for practitioners seeking to enhance client acquisition outcomes.