AI Driven Analysis of Customer Behavior in Mobile Telecommunications: Cultural Dynamics, Financial Insights, and Sustainable Development Perspectives - Ziadi, Nidhal; Ihkaf, Eya; Ziadi, Nihel; Slimane, Ichrak Ben (Journal of Cultural Analysis and Social Change, 2026)
This study explores the application of predictive data science techniques to anticipate customer behavior in the mobile telecommunications sector, integrating insights from finance and marketing. Leveraging advanced predictive models, including a multitask learning approach, the research is supported by an interactive web interface featuring a home- page, a Power BI dashboard, and a prediction page. The aim is to transform raw historical customer data into action- able insights for marketing teams, enabling accurate forecasts of mobile internet package activation and customers’ potential future value. Findings indicate that customer satisfaction and perceived sustainable value significantly influence subscription and recharge decisions, thereby enhancing loyalty and revenue generation. By emphasizing the synergy be- tween business needs, technological tools, and methodological frameworks, this work offers an innovative combination of theoretical and empirical approaches to advance practices within the telecommunications industry. Future research directions include incorporating real-time data streams and developing automated marketing recommendations to further optimize strategic effectiveness.