# Customer Segmentation Analysis > Use when segmenting customers and analyzing behavior. - Skill: `loopyluci/customer-segmentation-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add loopyluci/customer-segmentation-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/loopyluci/customer-segmentation-analysis/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: LoopyLuci (https://skillmd.com/u/loopyluci) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/loopyluci/customer-segmentation-analysis --- # Customer Segmentation and Analysis Segmenting customers into meaningful groups for targeted marketing, personalized experiences, and product decisions — from demographic and behavioral segmentation through RFM analysis and persona development. ## When to Use - Dividing customers into groups for targeted campaigns - Building customer personas for product and marketing decisions - Analyzing customer behavior patterns and purchase history - Implementing RFM (Recency, Frequency, Monetary) analysis - Identifying high-value segments for retention ## Segmentation Methods ```python from typing import Dict, List, Optional, Any from datetime import datetime, timedelta from collections import Counter import json class CustomerSegmentation: """Segment customers using multiple methodologies.""" @staticmethod def demographic( customers: List[Dict]) -> Dict[str, List]: """Segment by demographic attributes.""" segments = {} for c in customers: # Age group age = c.get('age', 0) if age < 25: group = '18-24' elif age < 35: group = '25-34' elif age < 45: group = '35-44' elif age < 55: group = '45-54' else: group = '55+' segments.setdefault(f'age_{group}', []).append(c) # Location region = c.get('region', 'unknown') segments.setdefault(f'region_{region}', []).append(c) # Summary return { group: len(members) for group, members in segments.items() } @staticmethod def behavioral( customers: List[Dict]) -> Dict: """Segment by purchase behavior.""" segments = { 'high_value': [], 'frequent': [], 'at_risk': [], 'new': [], 'one_time': [], } for c in customers: total_spent = c.get('total_spent', 0) order_count = c.get('order_count', 0) days_since_last = c.get('days_since_last_purchase', 999) account_age_days = c.get('account_age_days', 0) if total_spent > 1000 and order_count > 5: segments['high_value'].append(c) elif order_count > 10: segments['frequent'].append(c) elif days_since_last > 90 and order_count > 0: segments['at_risk'].append(c) elif account_age_days < 30: segments['new'].append(c) elif order_count == 1: segments['one_time'].append(c) return {k: len(v) for k, v in segments.items()} @staticmethod def lifecycle_stage( customers: List[Dict]) -> Dict: """Segment by customer lifecycle stage.""" stages = { 'exploration': 'First 30 days, exploring products', 'active': 'Regular purchasers, engaged', 'loyal': 'High repeat rate, brand advocates', 'declining': 'Decreasing engagement, fewer purchases', 'churned': 'No purchase in 90+ days', 'reactivated': 'Returned after dormant period', } result = {} for c in customers: days_since_last = c.get('days_since_last_purchase', 999) order_count = c.get('order_count', 0) account_age = c.get('account_age_days', 0) if days_since_last > 90 and order_count > 0: stage = 'churned' elif account_age < 30: stage = 'exploration' elif order_count > 5: stage = 'loyal' elif order_count > 1: stage = 'active' else: stage = 'declining' result[stage] = result.get(stage, 0) + 1 return {'stages': result, 'definitions': stages} ``` ## RFM Analysis ```python class RFMAnalyzer: """Recency, Frequency, Monetary analysis for customer segmentation.""" @staticmethod def analyze(customers: List[Dict]) -> List[Dict]: """Perform RFM analysis and assign scores (1-5).""" now = datetime.now() # Calculate raw RFM values rfm_data = [] for c in customers: last_purchase = datetime.fromisoformat(c.get('last_purchase_date', now.isoformat())) recency = (now - last_purchase).days frequency = c.get('order_count', 0) monetary = c.get('total_spent', 0) rfm_data.append({'id': c.get('id'), 'recency': recency, 'frequency': frequency, 'monetary': monetary}) # Score each dimension (1-5, where 5 is best) for dim in ['recency', 'frequency', 'monetary']: values = sorted([d[dim] for d in rfm_data]) # Lower recency is better; higher frequency/monetary is better reverse = dim != 'recency' for d in rfm_data: percentile = sum(1 for v in values if (v <= d[dim]) != reverse) / max(len(values), 1) d[f'{dim}_score'] = min(5, max(1, round(percentile * 5))) # Assign segments for d in rfm_data: r = d['recency_score'] f = d['frequency_score'] m = d['monetary_score'] if r >= 4 and f >= 4 and m >= 4: d['segment'] = 'Champions' elif r >= 4 and f >= 3 and m >= 3: d['segment'] = 'Loyal Customers' elif r >= 3 and f >= 2 and m >= 2: d['segment'] = 'Potential Loyalists' elif r >= 4 and f <= 2: d['segment'] = 'New Customers' elif r <= 2 and f >= 3 and m >= 3: d['segment'] = 'At Risk' elif r <= 2 and f >= 4 and m >= 4: d['segment'] = 'Cannot Lose' elif r <= 2 and f <= 2: d['segment'] = 'Hibernating' else: d['segment'] = 'Needs Attention' return rfm_data @staticmethod def segment_breakdown(rfm_results: List[Dict]) -> Dict: """Get summary of RFM segments.""" from collections import Counter segments = Counter(d['segment'] for d in rfm_results) return dict(segments.most_common()) ``` ## Persona Builder ```python class PersonaBuilder: """Build detailed customer personas from data.""" @staticmethod def create_from_data(customers: List[Dict], segment_name: str) -> Dict: """Create a persona representing a customer segment.""" if not customers: return {'name': segment_name, 'count': 0} avg_age = sum(c.get('age', 30) for c in customers) / len(customers) top_regions = Counter(c.get('region', 'Unknown') for c in customers).most_common(3) avg_spend = sum(c.get('total_spent', 0) for c in customers) / len(customers) common_sources = Counter(c.get('acquisition_source', '') for c in customers if c.get('acquisition_source')).most_common(2) return { 'name': segment_name, 'count': len(customers), 'demographics': { 'avg_age': round(avg_age, 0), 'top_regions': [r[0] for r in top_regions], 'gender_split': 'Varies', }, 'behavior': { 'avg_lifetime_value': round(avg_spend, 2), 'avg_orders': round(sum(c.get('order_count', 0) for c in customers) / len(customers), 1), 'top_acquisition_sources': [s[0] for s in common_sources], }, 'needs': [ 'Reliable customer support', 'Competitive pricing', 'Fast delivery/shipping', ], 'marketing_channel_preferences': ['Email', 'Social Media'], } ``` ## Common Pitfalls 1. **Too many segments** — 3-5 actionable segments beat 20 that can't be targeted; consolidate 2. **Static segments** — customer behavior changes; update segments quarterly 3. **Not linking to action** — segments without targeting strategies are academic exercises 4. **Over-relying on demographics** — behavioral segments predict future behavior better than demographics 5. **Small sample segments** — segments with <100 customers aren't statistically reliable ## Verification Checklist - [ ] At least 3 segmentation methods applied (demographic, behavioral, RFM) - [ ] Segments are mutually exclusive (no customer in multiple segments) - [ ] Each segment has a clear targeting strategy - [ ] RFM analysis completed with 1-5 scoring - [ ] Persona document created for top 3 segments - [ ] Segment size large enough to be actionable - [ ] Segmentation updated at least quarterly ## See Also - crm-sales-pipeline — acting on segments in sales - email-marketing-campaigns — targeting segments by email - digital-marketing-strategy — segment-based channel selection - business-metrics-kpis — measuring segment performance