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Regression • Clustering • Content Analytics
Spotify Content Performance Analysis
Analyzed a 952-song dataset to determine how playlist placement, audio features, and release timing influence streaming performance, then translated the findings into platform and promotion strategy.
Business Challenge
Understanding what actually drives music streams.
Streaming performance can be influenced by playlist support, release timing, artist characteristics, and audio features. The challenge was to identify which factors mattered most and convert them into practical promotional recommendations.
The Problem
- Audio features alone may not explain commercial performance.
- Playlist placement can create strong exposure effects.
- Seasonality may change when content performs best.
- Platform priorities require evidence, not intuition.
Project Goal
Quantify the relative influence of playlist placement, audio characteristics, and release timing, then recommend a promotion strategy based on statistically significant drivers.
Analytical Approach
Combining regression and clustering.
The project used complementary methods to explain performance and identify meaningful groups within the music catalog.
Regression Modeling
Built multiple linear regression models to estimate the relationship between playlist placement, audio features, timing, and stream outcomes.
Clustering
Grouped songs with similar performance and audio profiles to identify actionable content segments.
Feature Analysis
Compared the explanatory value of audio variables with exposure and timing variables.
Business Translation
Converted statistical findings into platform prioritization and season-specific promotion guidance.
Key Findings
Playlist placement and seasonality outperformed audio features.
The analysis showed that commercial exposure and timing had stronger relationships with streams than many intrinsic audio characteristics.
Analytical Findings
- Regression model achieved R² of 0.464.
- Playlist placement was a leading performance driver.
- Seasonality produced statistically significant effects.
- Audio features had weaker explanatory power than exposure variables.
Strategic Recommendations
- Prioritize playlist opportunities.
- Coordinate releases with stronger seasonal windows.
- Use platform-specific promotion plans.
- Treat audio features as supporting context rather than the main growth lever.
Methodology
A reproducible content analytics workflow.
The project moved from data preparation through modeling, segmentation, interpretation, and executive recommendation.
Process
- Data cleaning
- Exploratory analysis
- Regression modeling
- Clustering
- Hypothesis testing
- Business synthesis
Tools
- Python
- Pandas
- Scikit-learn
- Statistical modeling
- Data visualization
- Presentation design
Skills Demonstrated
Content analytics translated into marketing strategy.
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