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

1/7

songs_liked

| CSV

Description:
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

users

| CSV

Description:
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

| CSV

Description:
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

| CSV

Description:
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

| CSV

Description:
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

| CSV

Description:
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

| CSV

Description:
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

Engagement and Discovery Metrics Using DAX

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