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

Journal of Economics Finance and Accounting Studies, Год журнала: 2024, Номер 6(3), С. 178 - 186

Опубликована: Июнь 22, 2024

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

Язык: Английский

---

Enhancing Customer Satisfaction Analysis Using Advanced Machine Learning Techniques in Fintech Industry <br>

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

---

Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

---

Enhancing Credit Card Fraud Detection: A Comprehensive Study of Machine Learning Algorithms and Performance Evaluation

Journal of Business and Management Studies, Год журнала: 2024, Номер 6(3), С. 252 - 259

Опубликована: Июнь 13, 2024

Credit card fraud detection remains a significant challenge for financial institutions and consumers globally, prompting the adoption of advanced data analytics machine learning techniques. In this study, we investigate methodology performance evaluation various algorithms credit detection, emphasizing preprocessing techniques model effectiveness. Through thorough dataset analysis experimentation using cross-validation approaches, assess logistic regression, decision trees, random forest classifiers, Naïve Bayes K-nearest neighbors (KNN), artificial neural networks (ANN-DL). Key metrics such as accuracy, sensitivity, specificity, F1-score are compared to identify most effective models detecting fraudulent transactions. Additionally, explore impact different folds in on performance, providing insights into classifiers' robustness stability. Our findings contribute ongoing efforts develop efficient systems, offering valuable researchers striving combat effectively.

Язык: Английский

---

Optimizing Customer Segmentation in the Banking Sector: A Comparative Analysis of Machine Learning Algorithms

Journal of Computer Science and Technology Studies, Год журнала: 2024, Номер 6(4), С. 01 - 07

Опубликована: Авг. 30, 2024

Customer segmentation is a critical strategy in the banking sector, enabling banks to tailor their products and services meet diverse needs of customer base. This study explores application machine learning algorithms—K-Means Clustering, Hierarchical Gaussian Mixture Models (GMM)—for sector. The findings reveal that K-Means with silhouette score 0.62, highly effective for creating distinct easily interpretable segments, making it suitable scenarios requiring efficiency. Clustering offers deeper insights into relationships but less efficient large datasets. GMM provides most flexible approach, capturing complex overlapping behaviors, requires significant computational resources poses interpretability challenges. results underscore importance selecting appropriate algorithm based on objectives resource constraints, ultimately enhancing targeted marketing satisfaction

Язык: Английский

---

A comprehensive systematic review of machine learning in the retail industry: classifications, limitations, opportunities, and challenges

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Дек. 20, 2024

Язык: Английский

---