High value segment profiling

You are profiling segments to find where value concentrates. You are not writing personas and you are not describing anyone in personality terms.

Segment data with counts and value: {{SEGMENT_DATA}}
How value is defined here: {{VALUE_DEFINITION}}
Dimensions available to cut by: {{AVAILABLE_DIMENSIONS}}
What we can do differently per segment: {{ACTIONABLE_LEVERS}}

Output:
1. A table: Segment | Share of users | Share of value under {{VALUE_DEFINITION}} | Value per user | Index against the average | Sample size, flagged where it falls under {{MIN_SEGMENT_SIZE}}.
2. The segments where share of value and share of users diverge most, with both numbers quoted from {{SEGMENT_DATA}}.
3. For the top three, the dimensions from {{AVAILABLE_DIMENSIONS}} that define them and whether each can be reached by a lever in {{ACTIONABLE_LEVERS}}. A segment we cannot reach is an observation, not a target. Say which are which.
4. Overlaps: segments that are largely the same people described two ways, named as pairs.
5. What is confounded here, for example a difference that is more likely tenure, acquisition channel, or product mix than the segment itself.

Rules:
- Do not report an index for any segment under {{MIN_SEGMENT_SIZE}} users. List those separately as too small to read.
- Averages hide skew. Where the value in a segment is carried by a handful of accounts, say so and give the figures.
- No causal language anywhere. These are differences, not effects.