Incorta Copilot Tree Decomposition

Introduction to tree decomposition

Tree decomposition creates a hierarchical visualization that breaks down a measure across dimensions, showing how different factors contribute to your target metric. It buckets measures based on highest influence, allowing you to see the most significant factors at each level.

Some benefits include:

• Multi-level Analysis: See how factors interact across multiple dimensions
• Relative Importance: Understand which combinations of factors drive the most value
• Segmentation Insights: Identify high-value segments through multi-dimensional combinations
• Decision Support: Focus strategies on the most influential pathways

Tree composition command syntax

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/tree_explain [measure] for [dimension]

Example:

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/tree_explain @Total Amount for #Athletic Enthusiast

This creates a tree breakdown of the total amount metric specifically for the Athletic Enthusiast segment.

Alternative access

You can also access tree decomposition via suggested actions in the Key Driver analysis interface.

Interpresting tree decomposition results

The tree visualization shows:

• Root Node: Your primary measure
• Branches: Dimensions with significant influence
• Sub-branches: Values within each dimension
• Metrics: Values and percentages at each node showing the contribution to the parent node

Best Practices

1. Start with a specific segment: Target a particular value of interest for clearer insights
2. Balance tree depth: Too many levels can create overly complex visualizations
3. Look for unexpected patterns: Pay attention to surprising combinations
4. Compare across segments: Run multiple tree decompositions to compare different groups
5. Consider percentage and absolute values: Both metrics provide important context