The box plots depict an analysis of year-to-date streaming distribution for the Top 200 songs across genres by offering insights through two complementary scales:
Logarithmic Scale:
- Compresses wide streaming value ranges, making smaller genres like “Blues” and “Comedy” more visible.
- Enables direct comparison of smaller genres with dominant ones like “R&B/Hip-Hop” and “Pop.”
- Highlights variability within genres, with larger genres showing broader interquartile ranges and more outliers, indicating diverse performance levels.
Linear Scale:
- Emphasizes the dominance of larger genres, with streams for “R&B/Hip-Hop” and “Pop” far surpassing others.
- Clearly shows disparities, with smaller genres overshadowed by the massive scale of top-performing genres.
Together, these perspectives reveal key patterns and contributions from both large and niche genres, equipping your team with actionable insights for strategic decision-making.
Data Source: Music Connect by Luminate
Value boxes show the most recent data for the song, “A Bar Song (Tipsy)” by Shaboozey, which is the highest performing record of 2024. The interactive Highchart plot shows the trend over time for the performance of the song on streaming platforms since its release. Streaming data was downloaded from Music Connect by Luminate.
Data Week Ending
2024-11-28
Latest Streams YTD
931,870,883
This scatter plot illustrates the age distribution of listeners across various favorite genres, with each dot representing an individual response. It highlights demographic preferences, showing how different genres appeal to listeners of varying age groups. By visualizing listener density within age ranges, the scatter plot offers a more nuanced perspective than a bar graph, effectively revealing both the popularity and age diversity of each genre. Data Source: Kaggle
| Favorite Genre | 0-17 | 18-24 | 25-34 | 35-54 | 55+ |
|---|---|---|---|---|---|
| Classical | 19 | 16 | 9 | 5 | 4 |
| Country | 3 | 14 | 3 | 4 | 1 |
| EDM | 7 | 19 | 10 | 1 | 0 |
| Folk | 5 | 12 | 8 | 5 | 0 |
| Gospel | 0 | 1 | 0 | 0 | 5 |
| Hip hop | 8 | 15 | 7 | 3 | 1 |
| Jazz | 3 | 11 | 4 | 0 | 2 |
| K pop | 8 | 17 | 1 | 0 | 0 |
| Latin | 1 | 2 | 0 | 0 | 0 |
| Lofi | 3 | 4 | 3 | 0 | 0 |
| Metal | 11 | 38 | 29 | 10 | 0 |
| Pop | 28 | 56 | 17 | 9 | 4 |
| R&B | 5 | 22 | 3 | 1 | 4 |
| Rap | 7 | 13 | 1 | 0 | 1 |
| Rock | 33 | 69 | 46 | 21 | 19 |
| Video game music | 12 | 25 | 3 | 4 | 0 |
These bar charts highlight the top 25 artists with the largest monthly growth (left panel) and decline (right panel) in listeners for November 2024. Each bar represents an artist, with its height reflecting the magnitude of growth or decline in millions of listeners. The color gradient visually emphasizes the intensity of these changes, enabling a clear comparison of performance trends. Monitoring monthly shifts in listener engagement provides valuable insights into consumer listening habits and emerging patterns over time. Data Source: ChartMasters
Mariah Carey had the highest listener growth in November 2024 with 26.8M new listeners.
Ray Parker Jr. had the highest decline in monthly listeners in November 2024, losing over 9M listeners.
This line chart (time-series plot) visualizes the revenue trends of top US music streaming services starting from Q1 2020. These platforms generate revenue primarily through paid subscriptions, which offer ad-free music and unlimited access to music libraries. A significant portion of revenue also comes from ad-supported plans, where advertisements are shown to free users. Since 2020, music streaming services have experienced a steady increase in revenue due to several factors. The global shift to digital entertainment, accelerated by the COVID-19 pandemic, drove more users to subscription-based platforms as live events were canceled and people spent more time at home. Furthermore, the rise of personalized playlists, advanced music discovery algorithms, and exclusive content from popular artists have been key to attracting and retaining subscribers. Data Source: Statista
This dashboard was created using Quarto in RStudio, and the R Language and Environment.
The data used to create this dashboard were downloaded from:
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