segmentation-expert

name

segmentation-expert

description

Identify, analyze, and act on customer segments to uncover hidden patterns and understand behavior differences. Use when investigating KPI changes to understand which customers drove them, planning targeted initiatives, building customer personas, or analyzing performance differences across groups. Helps select meaningful segmentation dimensions, create actionable segments, analyze performance differences, calculate segment contributions, and generate segment-specific recommendations.

Segmentation Expert

Identify and analyze customer segments to uncover hidden patterns, understand behavior differences, and tailor strategies for maximum impact.

Core Framework: The Segmentation Process

Phase 1: Define Segmentation Dimensions

Main Dimension Categories:

1. Demographic (B2C: age, gender, location; B2B: company size, industry, funding stage) 2. Behavioral (usage level, feature adoption, session frequency, purchase patterns) 3. Acquisition (channel, campaign, referrer, first touchpoint) 4. Lifecycle (tenure, cohort, stage, product tier, upgrade history) 5. Psychographic (job to be done, use case, goals, pain points) 6. Firmographic (B2B: industry, revenue, employee count, tech stack)

Phase 2: Select Relevant Dimensions

Selection Criteria (Score 1-5 each):

1. Actionability: Can you do something different for this segment?
2. Measurability: Can you identify segment membership easily?
3. Size/Viability: Is segment large enough (typically >5%)?
4. Stability: Does definition remain consistent over time?
5. Differentiation: Do segments behave differently (>20% performance difference)?

Selection Process:

1. Brainstorm all possible dimensions (10-20)
2. Score each on 5 criteria (max 25 points)
3. Select top 3-5 dimensions (score >15/25)
4. Validate with data (check if differences exist)

Phase 3: Create Segments

Methods:

Rules-Based: Explicit rules define membership

• Example: "Power Users = >20 sessions/month AND use >5 features"
• Best for: Operational segmentation, clear behavioral groups

Value-Based: Segment by value delivered/generated

• Example: "High-Value = Top 20% by LTV"
• Best for: Prioritization, resource allocation

RFM (Recency, Frequency, Monetary): Classic for transaction businesses

• Champions: Recent, Frequent, High-Value
• At-Risk: Previously high-value, now less recent
• Best for: E-commerce, SaaS with usage tiers

Clustering (Statistical): Algorithm finds natural groupings

• K-means, hierarchical clustering, DBSCAN
• Best for: Exploratory analysis, persona development

Persona-Based: Segments based on goals, use cases, JTBD

• Best for: Product strategy, positioning

Phase 4: Analyze Segment Performance

For each segment, describe analysis in words:

Data to Collect:

• Segment size (number of users, % of total)
• Key metrics per segment (conversion, retention, engagement, LTV)
• Time trends for each segment
• Overlap with other segmentation dimensions

How to Analyze:

Analysis 1: Segment Size and Composition

• Chart type: Pie chart or stacked bar
• Show: % of total users or revenue per segment
• Compare: Current period vs previous period

Analysis 2: Performance Comparison

• Chart type: Grouped bar chart or comparison table
• X-axis: Segments
• Y-axis: Key metric (conversion rate, LTV, retention, etc.)
• Show: Performance difference between segments
• Highlight: Best and worst performing segments

Analysis 3: Trend Over Time

• Chart type: Multi-line chart
• X-axis: Time (weeks or months)
• Y-axis: Key metric
• Lines: One line per segment
• Look for: Divergence or convergence of segments

What to Look For:

Segment differences:

• Which segments perform significantly better/worse (>20% difference)?
• Is the difference consistent over time?
• Are differences statistically significant?

Segment contribution:

• Which segments drive most of the overall metric change?
• Calculate contribution: (Segment size × Performance difference)

Actionable patterns:

• What characteristics define high-performing segments?
• Can low-performing segments be improved or high-performing ones scaled?

Phase 5: Generate Recommendations

Framework:

For each segment, specify:

• Current Performance: What metrics show
• Root Cause: Why this segment performs this way
• Opportunity: What could be improved
• Action: Specific, testable intervention
• Expected Impact: Quantified improvement estimate
• Priority: Based on impact × feasibility

Segmentation Patterns

Pattern 1: Acquisition Channel Segmentation

Use when: Understanding marketing effectiveness

Dimensions: Organic, Paid, Referral, Partnership, Direct

Analysis approach:

• Compare conversion, retention, LTV by channel
• Calculate CAC and payback period by channel
• Identify highest quality channels

Pattern 2: Usage Level Segmentation

Use when: Understanding engagement

Dimensions: Power Users, Regular Users, Casual Users, Inactive

Rules example:

• Power: >20 sessions/month
• Regular: 10-20 sessions/month
• Casual: 1-9 sessions/month
• Inactive: 0 sessions/month

Analysis approach:

• Track distribution over time
• Analyze migration between segments
• Identify "aha moment" that moves users up

Pattern 3: Lifecycle Stage Segmentation

Use when: Optimizing customer journey

Dimensions: Trial, Active, At-Risk, Churned, Resurrected

Analysis approach:

• Track stage transitions
• Measure time in each stage
• Identify interventions that move users forward

Pattern 4: Value-Based Segmentation

Use when: Prioritizing retention/expansion

Dimensions: High-Value (top 20%), Medium-Value (middle 60%), Low-Value (bottom 20%)

Analysis approach:

• Calculate LTV per segment
• Analyze retention patterns by value tier
• Identify expansion opportunities

Advanced Techniques

Hierarchical Segmentation

Create segments within segments for deeper analysis

Example:

Level 1: Plan Tier
└─ Free Users
   └─ Level 2: By Engagement
      ├─ Active (target for conversion)
      └─ Inactive (nurture or ignore)
└─ Paid Users
   └─ Level 2: By Value
      ├─ High LTV (retain)
      ├─ Medium LTV (expand)
      └─ Low LTV (improve)

Segment Migration Analysis

Track movement between segments over time

Example analysis:

• % of power users who become casual (churn risk)
• % of casual users who become power (growth opportunity)
• Identify triggers for positive/negative migrations

Segment-Specific Funnels

Different conversion paths for different segments

Example:

• Enterprise: Demo → Trial → Negotiation → Contract
• SMB: Signup → Activation → Aha Moment → Paid

Analyze conversion rates separately for each segment.

Common Mistakes & Fixes

Mistake 1: Too Many Segments

• Problem: 20 segments, can't act on all
• Fix: Start with 3-5 high-level segments, refine later

Mistake 2: Segments Can't Be Targeted

• Problem: "Users who will churn next month"
• Fix: Use leading indicators you can identify early

Mistake 3: Static Segmentation

• Problem: Never revisit definitions
• Fix: Review quarterly, evolve as business evolves

Mistake 4: Equal Treatment of Unequal Segments

• Problem: Focus on largest segment regardless of value
• Fix: Prioritize by strategic value × actionability

Mistake 5: Confusing Correlation with Causation

• Problem: "Enterprise customers convert better because they're enterprises"
• Fix: Identify mechanism (onboarding support, budget pressure, clear use case)

Validation Checklist

Definition Quality:

• Each segment has clear, objective definition
• Segments are mutually exclusive (no overlap)
• Segments are collectively exhaustive (cover everyone)
• Can assign any user to segment programmatically

Business Relevance:

• Segments differ meaningfully (>20% on key metrics)
• Can take different actions for each segment
• Segments are large enough (typically >5%)
• Stakeholders understand segmentation

Operational Feasibility:

• Can identify segment membership in real-time
• Can report on segments in dashboards
• Can target segments in campaigns/product
• Team has resources to act on segments

Analytical Validity:

• Validated with sufficient data
• Differences are statistically significant
• Patterns persist over time
• Controls for confounding factors

Related Agents & Skills

Prerequisites:

• 🌳 KPI Tree Architect - Segment KPIs from tree
• 📊 KPI Definition Specialist - Define metrics per segment

Use together:

• 🔍 Root Cause Investigator - Segment to find root cause
• 📅 Cohort Analysis Specialist - Segment + time = cohorts
• 🎯 Mix Effect Analyzer - Quantify segment contributions
• 📋 Analysis Planner - Plan segment analysis properly

For outputs:

• 📊 Chart & Visualization Advisor - Visualize segment differences
• 📝 Executive Summary Writer - Communicate insights

Success Metrics

Segmentation is effective when:

• ✅ Can identify 3-5 actionable segments
• ✅ Team talks about segments in planning
• ✅ Product roadmap prioritizes by segment
• ✅ Marketing campaigns tailored by segment
• ✅ Segments drive meaningful decisions

Key Principles

From "The Power of Analytics":

• "Segmentation allows you to drill down and extract hidden patterns"
• "Find segments that behave differently enough to warrant different treatment"
• "Always ask: can I do something different for this segment?"
• "Segmentation helps you move from 'what happened' to 'what happened to whom'"

See references/segmentation_dimensions.md for complete dimension catalog See references/segmentation_methods.md for detailed method comparisons