segmentation-analysis

• SKILL.mdcore · 2,893 bytes
• assets/segment_profile_template.mddoc · 1,719 bytes
• references/segmentation_approaches.mddoc · 2,850 bytes
• scripts/segmentation_runner.pyruns · 6,252 bytes

# Segmentation Analysis

# When to use
- The team needs to understand who the best customers are and what distinguishes them
- Marketing wants distinct groups to target with different messages or offers
- Product needs to prioritise features based on high-value user behaviour patterns
- Churn is high and the team needs to identify at-risk users before they leave
- An existing segmentation feels arbitrary and needs data validation or improvement

# Process
1. **Define the segmentation goal** — clarify what decisions the segments will inform (product roadmap, marketing campaigns, retention programs). The goal determines which variables matter and how many segments are useful (typically 3–7). See `references/segmentation_approaches.md`.
2. **Select and prepare variables** — choose 3–7 attributes or behaviours that vary across users and relate to the business outcome. Handle missing values and scale continuous variables. Remove outliers only if they would distort cluster centroids.
3. **Run the segmentation** — for data-driven segmentation, use k-means clustering via `scripts/segmentation_runner.py`. For rule-based segmentation, apply the business logic rules and validate that segments are distinct and non-overlapping.
4. **Profile each segment** — compute the mean and median for each variable by segment, expressed as % above/below the overall average. Identify the 2–3 defining characteristics of each segment and assign a descriptive name.