A Data-Driven Analysis of Song Lifecycle, Playlist Rotation, and Content Maturity in Spain's Top 50

The rapid evolution of digital music platforms has transformed playlist rankings into dynamic environments where songs continuously enter, grow, peak, mature, and decline. Understanding these lifecycle patterns is essential for analyzing content performance, playlist stability, and audience engagement. This study presents a data-driven analysis of song lifecycle dynamics, playlist rotation, and content maturity using observations from Spain's Top 50 music chart. The proposed analytical framework applies data preprocessing, lifecycle construction, stage classification, churn analysis, and content attribute analysis to examine the behavior of songs throughout their chart presence. Key lifecycle indicators, including entry date, peak position, days to peak, chart longevity, and playlist retention, are used to evaluate song performance over time. The study further investigates playlist churn and rotation patterns while comparing content characteristics such as explicit status, release type, duration, and popularity. The results are consolidated into key performance indicators and presented through an interactive Streamlit dashboard to support intuitive exploration of lifecycle and playlist trends. This work demonstrates how Python-based data analytics and visualization techniques can transform raw music-chart data into meaningful insights about content longevity, maturity, and playlist dynamics. The project was developed as part of a data analytics internship project and provides a structured foundation for future research in music analytics, lifecycle prediction, and data-driven content intelligence.