# Segment Analysis

> Discover natural data segments and analyze group differences: clustering-based segmentation, cross-tabulation, statistical comparison between groups, and segment profiling. Use when looking for natural groupings or comparing subpopulations.

- **Type:** Skill
- **Install:** `agentstack add skill-ericwang915-data-scientist-skills-segment-analysis`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ericwang915](https://agentstack.voostack.com/s/ericwang915)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ericwang915](https://github.com/ericwang915)
- **Source:** https://github.com/ericwang915/data-scientist-skills/tree/main/ds-exploratory-analysis/skills/segment-analysis

## Install

```sh
agentstack add skill-ericwang915-data-scientist-skills-segment-analysis
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Segment Analysis

## Purpose
Discover natural segments in your data and characterize how groups differ. Combines unsupervised clustering with statistical comparison to reveal meaningful subpopulations.

## How It Works

### Step 1: Identify Segments
- **Predefined segments**: Analyze existing categories (e.g., by plan tier, region)
- **Data-driven segments**: Discover groups using K-means, DBSCAN, or hierarchical clustering
- Optimal cluster count via elbow method, silhouette score, gap statistic

### Step 2: Profile Each Segment
- Size: count and percentage of total
- Demographics: distribution of key features per segment
- Behavior: mean/median of behavioral metrics per segment
- Distinguishing features: what makes each segment unique

### Step 3: Compare Segments
- Statistical tests for group differences (ANOVA, Kruskal-Wallis, chi-squared)
- Effect sizes (Cohen's d, eta-squared)
- Pairwise post-hoc comparisons with multiple testing correction

### Step 4: Actionable Insights
- Name each segment with a descriptive label
- Rank segments by business value or analytical interest
- Recommend targeted actions per segment

## Usage Examples

```
"Segment our users based on engagement patterns and tell me
how each segment differs in conversion rate"
```

```
"Compare feature usage across free, pro, and enterprise tiers —
which features drive upgrades?"
```

## Output Format

- **Segment Profiles**: Descriptive summary of each segment
- **Comparison Table**: Side-by-side metrics with statistical significance
- **Visualizations**: Segment distributions, radar charts, parallel coordinates
- **Python Code**: Segmentation and profiling script

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ericwang915](https://github.com/ericwang915)
- **Source:** [ericwang915/data-scientist-skills](https://github.com/ericwang915/data-scientist-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ericwang915-data-scientist-skills-segment-analysis
- Seller: https://agentstack.voostack.com/s/ericwang915
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.