Isioma Isoko

“Insights and Trends in Spotify Streaming Data” Project Overview This capstone project focuses on the Insights and Trends in Spo fy Streaming Data. It explores pa erns in user listening behavior, ar st popularity, and song performance through interac ve data visualiza on and trend analysis. Portfolio link: https://isiomaisoko.github.io/isiomaisoko/ “Insights and Trends in Spotify Streaming Data” The project applies data analy cs, visualiza on tools, and performance tracking techniques to monitor and interpret streaming ac vi es. The analysis evaluates key performance indicators (KPIs) such as total play dura on, skip rates, and me-based listening behaviors to derive ac onable insights for content op miza on and audience engagement. The analytical approach in this project is organized into four central focus areas: 1. Ar st Performance Analysis: Iden fies top ar sts based on total play dura on to assess listener preferences. 2. Song Performance and Skip Rate Evalua on: Analyzes most played songs alongside skip tendencies to measure user engagement. 3. Monthly Streaming Trend Analysis: Tracks fluctua ons in average streaming across months to detect seasonal listening pa erns. 4. Time-of-Day Listening Review: Examines when users stream most frequently (morning, night, evening, or a ernoon). This analysis uses data-driven insights to reveal how streaming pa erns and listener behavior can inform music marke ng strategies, enhance playlist cura on, and support audience engagement and reten on efforts. Project Objec ve To conduct a comprehensive analy cal and performance review of Spo fy streaming data, measuring trends in listening habits, ar st and song performance, and engagement metrics. The objec ve is to leverage these insights to:  Understand listener preferences and behavior pa erns.  Evaluate the effec veness of content engagement.  Provide recommenda ons for improving pla orm user experience, marke ng focus, and content visibility.  Support decision-making for ar st promo on and audience targe ng strategies. Key Insights 1. Most Played Ar sts  John Mayer emerged as the most streamed ar st of 2024, followed by The Killers and The Beatles. Portfolio link: https://isiomaisoko.github.io/isiomaisoko/ “Insights and Trends in Spotify Streaming Data”  This indicates a listener preference toward classic rock and mellow acous c genres.  Ar sts like Kendrick Lamar and Billy Joel also maintained consistent engagement, reflec ng genre diversity among listeners. 2. Most Played Songs and Skip Rate  Tracks such as “The Return of the King” and “The Fellowship Reunited” had the highest total play me.  However, some songs (e.g., “Why Did It Have to Be Me”) showed higher skip rates, indica ng varying user engagement across tracks.  High play counts with low skip rates suggest strong listener reten on and track appeal. 3. Average Streaming by Month  Peak streaming months were February and August, while the lowest occurred in June and July.  This pa ern could reflect seasonal behavior, with higher ac vity during leisure or holiday periods.  The analysis highlights poten al meframes for promo ng new releases or curated playlists. 4. Time of Day Listening  Listening ac vity was highest in the morning and declined steadily toward the a ernoon.  This trend suggests that users are more engaged during early hours, possibly during commutes or work prepara on mes.  Understanding these me-of-day trends can help tailor recommenda on algorithms and playlist ming for be er engagement. Conclusion This analy cal review reveals how data visualiza on and performance metrics can drive ac onable insights into user engagement and streaming behavior. By iden fying key pa erns across ar sts, songs, me, and seasonality, the project demonstrates the importance of data-driven decision-making in op mizing content delivery, marke ng strategy, and overall pla orm performance. Portfolio link: https://isiomaisoko.github.io/isiomaisoko/