Prediction of the Impact and Performance of FinTech Companies' Advertisements on Customer Acquisition and Loyalty Using Deep Learning Techniques

Abstract

With the rapid growth of the financial technology (FinTech) industry, digital advertising has become one of the key tools for customer acquisition and loyalty. This study examines the impact of FinTech companies' digital advertisements on customer acquisition and loyalty using deep learning techniques. To achieve this, data related to customer interactions with advertisements were collected from FinTech companies' databases and online questionnaires. Subsequently, a deep learning model was trained using Deep Neural Networks (DNN) to predict customer loyalty.

The results indicated that the proposed model successfully predicted customer loyalty with high accuracy. Model evaluation metrics, including Mean Squared Error (MSE) and Coefficient of Determination (R²), confirmed that the model effectively identified customer behavioral patterns and could generalize them to new data. Additionally, the analysis confirmed that Conversion Rate and Purchase Amount are the most influential factors in customer loyalty.

Furthermore, an examination of targeted advertising effects demonstrated that personalized advertising and optimized ad content based on customer behavioral data significantly enhance engagement and loyalty. A comparison of this study’s findings with domestic and international research confirmed that deep learning models play a crucial role in improving advertising strategies and increasing the return on investment (ROI) for FinTech companies. Based on these findings, it is recommended that FinTech companies leverage deep learning models to optimize digital advertising, increase conversion rates, and accurately target high-value customers. This approach can lead to enhanced advertising effectiveness, reduced marketing costs, and strengthened customer loyalty in the FinTech industry.