What is Unsupervised Learning? Examples in Analytics

Unsupervised learning

Unsupervised learning uncovers hidden patterns in unlabeled analytics data, enabling clustering, anomaly detection, and dimensionality reduction.

Importance of Unsupervised Learning in Analytics

• Pattern discovery

  Unsupervised learning algorithms detect hidden patterns and structures in datasets without predefined labels, enabling analysts to uncover insights that may not be visible through manual analysis.

  • Benefits:

    Automates exploration of large datasets, revealing trends and associations.

  • Considerations:

    Requires careful preprocessing and interpretation since results may not map directly to business metrics.

• Customer segmentation

  By grouping users with similar behaviors or characteristics, businesses can tailor marketing strategies and personalize experiences.

  • Use case:

    Segmenting website visitors based on session duration and page interactions.

  • Impact:

    Improves targeting accuracy and marketing ROI.

• Anomaly detection

  Algorithms identify data points that deviate significantly from the norm, helping detect fraud, system failures, or unusual user activities.

  • Examples:

    Spotting spikes in traffic that indicate bot activity.

  • Challenges:

    Balancing sensitivity to catch anomalies without generating false positives.

Key Techniques in Unsupervised Learning

• K-means clustering

  Partitions data into k pre-defined clusters by minimizing variance within each cluster.

  • Pros:

    Simple, scalable, and efficient for large datasets.

  • Cons:

    Requires specifying k and sensitive to initial centroid placement.

• Hierarchical clustering

  Builds a tree of clusters using either agglomerative or divisive approaches.

  • Benefits:

    Does not require a preset number of clusters and provides a dendrogram.

  • Drawbacks:

    Computationally intensive for large datasets.

• Dbscan

  Density-Based Spatial Clustering of Applications with Noise groups points closely packed while marking outliers as noise.

  • Strengths:

    Detects arbitrarily shaped clusters and handles noise.

  • Limitations:

    Requires careful tuning of density parameters.

• Principal component analysis (pca)

  Reduces dimensionality by projecting data onto principal components that explain maximum variance.

  • Advantages:

    Simplifies data visualization and reduces computational cost.

  • Considerations:

    Components may be hard to interpret and can discard meaningful variation.

Implementing Unsupervised Learning with Analytics SaaS Tools

• Plainsignal implementation

  PlainSignal provides cookie-free analytics with simple integration allowing event clustering and pattern analysis.

  • Setup:

    Add the PlainSignal script to your site header:

    <link rel=\"preconnect\" href=\"//eu.plainsignal.com/\" crossorigin />
    <script defer data-do=\"yourwebsitedomain.com\" data-id=\"0GQV1xmtzQQ\" data-api=\"//eu.plainsignal.com\" src=\"//cdn.plainsignal.com/PlainSignal-min.js\"></script>
    

  • Features:

    Enables session clustering and anomaly dashboards without cookies.

• Google analytics 4 (ga4) implementation

  GA4’s built-in AI features support anomaly detection and predictive analytics.

  • Activation:

    Enable ‘Anomaly Detection’ in the GA4 Analysis Hub and use Exploration reports for clustering user segments.

  • Considerations:

    Data sampling and privacy settings can affect the accuracy of AI-driven insights.

• Integration tips

  Combine outputs from multiple platforms to enrich analysis.

  • Data export:

    Use GA4’s BigQuery export and PlainSignal’s API to centralize datasets.

  • Cross-tool validation:

    Compare clusters and anomalies detected by each tool to improve model reliability.

Real-World Applications and Examples

• Website user segmentation

  Group anonymous visitors by behavior patterns like session length and navigation paths.

  • Business impact:

    Tailored content delivery increases engagement and conversion rates.

  • Implementation:

    Run K-Means clustering on session data using exported GA4 logs.

• Anomaly detection in traffic

  Identify unusual traffic spikes or drops to preempt server issues or detect malicious activity.

  • Use case:

    Alert when traffic deviates more than two standard deviations from the weekly average.

  • Tools:

    Leverage GA4 anomaly detection and PlainSignal alerts.

• Feature reduction for reporting

  Simplify dashboards by reducing correlated metrics into principal components.

  • Benefits:

    Easier visualization and faster report generation.

  • Approach:

    Apply PCA in external tools after exporting data from GA4.

Related terms