Engagement and Discovery Metrics Using DAX
Group Category: Use Case
Product Category: BI Reporting & Analytics
Sub Category: Power BI
Unlock rich behavioral insights from your music streaming platform with DAX-driven engagement and discovery analytics.
Business Overview:
Engagement and Discovery Metrics Using DAX helps you explore how users interact with music, playlists, and social features on a streaming platform. Tailored for data professionals, this product provides advanced Power BI use cases that reveal listening preferences, music discovery patterns, and collaborative behaviors. You'll use DAX to build user engagement scores, calculate genre affinity, and analyze playlist virality—key inputs for retention, personalization, and growth strategies.
Product Highlights:
- A structured guide featuring 5 DAX-powered behavioral use cases for music engagement analysis
- Covers discovery rates, content affinity, playlist interactions, and user engagement scoring
- Based on a realistic streaming dataset with listening, social, and content data
- Perfect for Power BI users, product managers, and digital media analysts
- Ideal for building advanced dashboards and user intelligence layers
Learning Outcome:
By completing these use cases, you’ll gain skills in:
- Measuring user engagement using multi-factor DAX scoring
- Analyzing music discovery behavior and affinity toward genres
- Understanding collaborative behaviors via playlist follows
- Identifying high-value users for retention and marketing actions
- Applying DAX filters and context transitions to behavioral data
users
| CSVDescription:
This table serves as the central identity map for platform users, enabling user-level analysis and cross-table relationship building.
- Contains user IDs, profile information, and demographic details
- Enables linking listening behavior, payments, subscriptions, and playlist activity
- Essential for computing user-level metrics such as lifetime value, churn, and engagement
- Forms the base for audience segmentation and behavior modeling
- Supports personalized recommendations and retention strategies
user_listening_history
| CSVDescription:
This file logs every listening session by users, capturing granular behavior over time.
- Records each play event with song ID, user ID, and timestamp
- Acts as the primary source for engagement, discovery, and activity metrics
- Enables trend analysis like peak listening hours and daily/weekly habits
- Supports affinity modeling, content personalization, and session scoring
- Key to understanding platform usage and music consumption patterns
user_playlist_follows
| CSVDescription:
This dataset logs which users follow which playlists, offering insights into social music discovery.
- Tracks user interactions with playlists created by others
- Enables measurement of playlist reach, community adoption, and follower density
- Critical for collaborative engagement scoring and influencer tracking
- Used in identifying popular playlists and community-driven trends
- Enhances models for social recommendation and playlist virality
songs_liked
| CSVDescription:
This file stores user likes for songs, reflecting direct preference signals and emotional resonance.
- Captures explicit feedback on content beyond passive listening
- Key input for engagement scoring and content relevance modeling
- Supports algorithms for music recommendations and user mood tagging
- Useful for measuring user satisfaction and music impact
- Can be combined with listening data to build holistic user profiles
songs
| CSVDescription:
This dataset contains metadata for all songs on the platform, forming the foundation of the content catalog.
- Includes song IDs, titles, and relationships to artists and genres
- Central to analyzing user interactions with content at the track level
- Supports genre analysis, artist performance, and catalog depth insights
- Used in building recommendation systems and play distribution metrics
- Complements listening history and genre mapping for behavioral clustering
song_genres
| CSVDescription:
This table maps songs to their respective genres, enabling content classification and taste profiling.
- Links each song to one or more genre categories
- Facilitates genre-based affinity scoring and preference detection
- Crucial for understanding content diversity and listener segmentation
- Supports editorial curation and genre-specific marketing campaigns
- Enhances personalization and content discovery workflows
playlists
| CSVDescription:
This file tracks playlists created by users, including metadata like playlist IDs and creation timestamps.
- Captures user-generated content behavior and curation patterns
- Supports metrics such as playlist popularity, activity trends, and social influence
- Enables tracking of creative engagement and content sharing
- Helps identify potential playlist influencers or community leaders
- Linked with follows and listening data for virality and engagement analysis
$1.50 $1.00 33% OFF
About this Dataset:
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