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/segmentation-analysis

@fcb0454

Customer/user segmentation with actionable insights. Use when identifying distinct customer groups, analyzing segment-specific behavior, profiling high-value segments, or testing segmentation hypotheses.

Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/segmentation-analysis

This session only. Nothing lands on disk.

assetssegment_profile_template.md

≈430 tokens on demand. Your agent reads this file only when SKILL.md points to it.

Segment Profile

Analysis: [analysis name] Segmentation dimension: [e.g., pricing plan, acquisition channel, industry] Period: [date range] Analyst: [name] Date: [YYYY-MM-DD]


Population overview

Segment N Share Primary metric Index
[segment 1] [n] [%] [value] [100-based index]
[segment 2] [n] [%] [value] [index]
[segment 3] [n] [%] [value] [index]
Overall [n] 100% [value] 100

Individual segment profiles

[Segment 1 name]

Size: [n] users ([%] of total) Primary metric: [value] (index: [n])

Key characteristics — where this segment over/under-indexes:

Metric Segment value Overall value Index
[metric] [value] [value] [index]
[metric] [value] [value] [index]
[metric] [value] [value] [index]

Hypothesised behaviour: [Why does this segment behave this way? What need or context drives it?]

Recommended action: [What product, marketing, or CS intervention makes sense for this segment?]


[Segment 2 name]

Size: [n] users ([%] of total) Primary metric: [value] (index: [n])

Key characteristics:

Metric Segment value Overall value Index
[metric] [value] [value] [index]

Hypothesised behaviour: [...]

Recommended action: [...]


Key findings

  1. [Most important finding across segments]
  2. [Second finding]
  3. [Third finding]

Recommendations

Segment Action Expected outcome Owner Date
[segment] [action] [outcome] [owner] [date]

Template: segment_profile_template.md

Source: SKILL.md on GitHub

No alerts16d4 checks · Risk SAFE
  • Gen Agent Trust Hub16d

    The skill provides a framework for customer segmentation analysis, including a Python script for profiling data segments and templates for reporting results. No security issues were detected; the provided code uses only standard Python libraries for statistical calculations and file I/O.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at fcb0454. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 5 days ago.

Activeupdated 5 months ago

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