Machine Learning Model in Digital Marketing Strategies for Customer Behavior: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making

Abstract

In the realm of digital marketing for the banking industry, the integration of deep learning methodologies, particularly Convolutional Neural Networks (CNNs) such as VGG16, Resnet50, and InceptionV3, has revolutionized strategic decision-making and customer satisfaction. This study explores how deep learning models leverage neural networks with multiple layers to analyze vast and complex datasets, uncovering intricate patterns in customer behavior and preferences. By enhancing customer segmentation, optimizing campaign performance, and refining personalized experiences, CNNs empower banks to make precise, data-driven decisions that elevate customer satisfaction and loyalty. Comparative analyses demonstrate CNNs' superior performance over traditional models like Random Forest and Logistic Regression, achieving accuracies up to 89% and F1 scores of 88%, thereby highlighting their transformative potential in reshaping digital marketing strategies within the banking sector. This research underscores the critical implications of adopting advanced deep learning techniques to meet the evolving demands of customers in today's dynamic digital landscape.