Optimizing Customer Engagement in Fintech Marketing: A Telecom-Centric Approach to Precision Targeting Using Mobile App Data

In modern advertising strategies, leveraging mo-bile app usage behavior for precision targeting has become essential, particularly within the Fintech marketing sector of the telecommunications industry. Telecom companies, with their extensive customer data and robust digital infrastructure, are well-positioned to offer tailored financial products and services. However, traditional methods of customer segmentation, credit scoring, and propensity scoring encounter significant challenges, limiting their ability to accurately identify and engage high-value users. This study addresses these limitations by introducing an innovative approach centered on dynamic feature engineering applied to mobile app data. By deeply mining app usage patterns and generating new variables through the integration of diverse data sources, the proposed method enhances both customer seg-mentation and model precision. The implementation of dynamic feature engineering significantly improves the accuracy and effectiveness of precision targeting, enabling telecom companies to better engage high-response users and optimize marketing resource allocation. This approach not only overcomes existing bottlenecks but also fosters deeper customer engagement and drives superior business outcomes in Fintech marketing.