User Segments

K-Means clustering of ~800K real users by what they do — recency, saves, subscriptions, profile completion, and declared interests. Each dot is one real user. Demographics (role, provider, tenure) are excluded from clustering.

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Daily avg sitewide users
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Engaged (beyond passive browsing)
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Site engagement rate
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Newsletter opens (parallel channel)
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Segment Daily Avg % of Total Definition Boundary Rule

User Clusters (PCA Projection)

Each point is one real user (equal 1,000-per-cluster sample of ~805K accounts), projected to 2D via Principal Component Analysis over 28 activity-only features. Colours are natural K-Means clusters. Grey lines are the cluster-separating Voronoi boundaries — the exact decision frontier between neighbouring cluster centroids in PC space.

PC1 Loadings

The linear combination of activity features that defines the x-axis.

PC2 Loadings

The linear combination of activity features that defines the y-axis.

Feature Importance (Eta-Squared)

Fraction of each feature's variance explained by the cluster assignments. Higher = stronger driver of separation.

Cluster Profiles (Normalised)

Normalised activity signature per cluster. Each cluster has a distinct behavioural shape.

Rule-Based Funnel

Static threshold segmentation for the engagement funnel. Newsletter runs as a parallel channel.

Weekly Trends

Engagement channel volumes over time (Q1 2026).

Segment Transitions

From / To Trigger GA4 Signal Intervention
Passive to Light First community surface visit Homeroom or A&A page view Stronger CTAs on article pages
Light to Active First card click Discovery card click event Improve card relevance and placement
Active to Contributor Content creation WP question/reply or upvote click Lower friction for first contribution
Any to Newsletter Opens an email SendGrid unique_open Cross-promote at engagement peaks