The Segmentation Explorer helps you uncover valuable user insights by analyzing patterns through a tree-like segmentation structure. Each level represents a new way to split your users, enabling deep analysis and actionable findings.
In this guide, we’ll explore how to use the Segmentation Explorer with examples based on a music streaming service. You’ll see how to build segmentation trees, interpret insights, and plan next steps.
What You’ll Learn:
Identifying users most likely to adopt premium features.
Understanding content preferences among free users.
Finding risky users who need re-engagement.
How the Segmentation Explorer Works
The Segmentation Explorer uses a tree-like structure where:
The root is your starting segment (e.g., Trial Users).
Each level represents a new way to split users.
Each node shows the number of users in this block and their percentage from the root segment.
You can add segments at each level to grow the tree.
The tool reveals meaningful sub-segments that help shape campaigns and strategies.
Example 1: Identifying Premium Feature Adopters
Hypothesis:
Users with higher premium feature adoption are more likely to convert to paid subscriptions.
Step 1: Select Root Segment
Root: Trial Users (47,532 users)
Step 2: Add First Level Segments
Premium Feature Adoption:
High Adoption: 14,328 users
Medium Adoption: 18,764 users
Low Adoption: 14,440 users
Step 3: Add Second Level Segments (High Adoption Group)
Time of Use (Listening Times):
Working Hours (9 AM - 5 PM): 5,139 users
Morning & Evening (6 AM - 9 AM, 5 PM - 8 PM): 6,402 users
Weekend & Night (8 PM - 6 AM): 2,787 users
Insight:
Users with High or Medium Adoption during specific time slots offer great personalization opportunities:
Working Hours users may prefer productivity playlists.
Morning & Evening users might enjoy motivational content.
Weekend & Night listeners could appreciate party or relaxation playlists.
Example 2: Identifying Risky Users
Hypothesis:
Low-engagement paying users are at risk of churning, and their risk levels may vary by country.
Step 1: Select Root Segment
Root: Paying Users (28,764 users)
Step 2: Add First Level Segments
Engagement Level:
Highly Engaged: 16,492 users
Moderately Engaged: 7,268 users
Low Engagement (At Risk): 5,004 users
Step 3: Add Second Level Segments (Low Engagement Group)
Country:
US: 2,153 users
UK: 1,785 users
Canada: 1,066 users
Insight:
Most at-risk users are from the US, making it a critical market for re-engagement campaigns. Consider offering personalized promotions, exclusive content, or timely reminders for these users.
Example 3: Understanding Content Preferences
Hypothesis:
Users with distinct content preferences respond better to personalized experiences.
Step 1: Select Root Segment
Root: Free Users (69,435 users)
Step 2: Add First Level Segments
Content Type Preference:
Music Lovers: 34,941 users
Kids’ Songs Listeners: 19,372 users
Podcast Fans: 15,122 users
Step 3: Add Second Level Segments (Music Lovers Group)
Device Used:
Mobile: 19,278 users
Desktop: 10,378 users
Tablet: 5,285 users
Insight:
The largest group of Music Lovers uses Mobile Devices, indicating an on-the-go listening pattern. Consider mobile-focused campaigns, in-app promotions, or personalized playlists to encourage subscriptions and boost engagement.
Next Steps
Start using the Segmentation Explorer to create meaningful segments, understand user behavior, and take action with personalized playbooks.