existing-customer-data

name	existing-customer-data
description	Analyze existing customer/CRM data: RFM segmentation, behavioral clustering, churn patterns, LTV analysis, upgrade patterns. OPTIONAL pillar — skip if pre-launch, no CRM access, or <50 customers. Use when: client has >50 customers with CRM data available and wants data-driven ICP refinement, churn reduction, or segment optimization. Produces Champions profile, RFM segments, churn patterns, LTV by segment, and actionable recommendations for ICP/positioning/content. NOT for: pre-launch companies, assumption-based ICP (use niche-discovery-100x), or general market research (use market-intelligence).
metadata	{"author":"Alfonso Sainz de Baranda (Growth4U)","version":"2.0","system":"SanchoCMO","phase":"1","pillar":"7","optional":true,"skip_if":"pre-launch OR no CRM access OR < 50 customers","updated":"2026-02-27","changes":"v2 — Restructured per skill-creator principles. SKILL.md lean (~110 lines). References created."}
context_required	["brand/{slug}/company-brief/company-brief.current.md","brand/{slug}/go-to-market/ecps/ecps.current.md","brand/{slug}/go-to-market/ecps/ecps.current.md"]
context_writes	["brand/{slug}/go-to-market/existing-customer-data/existing-customer-data.current.md"]

Existing Customer Data — Customer Intelligence (OPTIONAL)

OPTIONAL — Skip if pre-launch or no CRM. Analiza customer data para identificar best customers, segments, churn patterns, y upgrade triggers.

Input: CRM export / API / manual summary (>50 customers) Output: Customer intelligence → brand/{slug}/customer-data/customer-data.current.md

References

Archivo	Cuándo leer	Contenido
hydration.md	SIEMPRE — Step 0 obligatorio	Mapeo de campos upstream → esta skill
prompt.md	SIEMPRE — fuente de verdad	Template de output completo
checklist.md	Antes de entregar — self-QA	Ítems de verificación
concepts.md	Si necesitas RFM, clustering, churn methodology	Métodos, skip conditions, edge cases
schema.md	Si necesitas el schema de campos	Estructura de datos

---

Flujo de Ejecución

0. Context Hydration (OBLIGATORIO — antes de cualquier pregunta)

• Lee _system/skills/context-hydration-protocol.md para el patrón genérico
• Lee references/hydration.md para el mapeo específico de esta skill
• Lee TODOS los docs en context_required
• Pre-rellena campos según hydration_map
• Presenta datos heredados al usuario: "De [fuente] ya tengo X. ¿Correcto?"
• Solo pregunta campos listados en "Campos genuinamente nuevos"

1. Check Prerequisites

• Customer count > 50, CRM access, minimum fields (email, signup, plan, MRR)
• Si NO se cumplen → mark as skipped, proceed without this pillar

2. Obtain Data

• Option A: CRM Export (CSV) — user provides
• Option B: CRM API (HubSpot/Salesforce/Pipedrive)
• Option C: Manual Summary — limited analysis

3. Execute Analysis (ver references/concepts.md para detalles)

• RFM Segmentation: Classify into Champions, Loyal, At Risk, Lost, New
• Best Customer Profile: Identify Champions characteristics (tier, size, industry, use case)
• Behavioral Clustering: 3-5 clusters by usage patterns
• Churn Patterns: When, why, who, triggers, early warning signals
• LTV Analysis: Per segment, payback period
• Upgrade Patterns: Free→paid conversion, tier progression triggers

4. Generate Recommendations

• ICP refinement: Focus on Champions segment characteristics
• Positioning: What Champions value → use in messaging
• Content strategy: Address churn triggers
• Outreach: Target lookalikes of Champions

5. Self-QA (OBLIGATORIO)

• Lee references/checklist.md
• Privacy check: 0 PII in output (aggregated only)
• 0 ❌ antes de entregar
• Metadata: <!-- Self-QA: PASS | fecha | items: X✅ Y⚠️ 0❌ -->

6. Guardar con versionado

• Ruta: brand/{slug}/customer-data/customer-data.current.md
• Backup + versionado + history.json
• Link: <MC_BASE>/docs/brand/{slug}/customer-data/customer-data.current.md

---

Cross-Pillar Data Flow

Dato	Lo consume
Champions profile	niche-discovery-100x (ICP criteria)
Segment characteristics	positioning-messaging (proof points)
Churn triggers	content strategy (topics), email-sequences
LTV by segment	budget-constraints (pricing tiers)
Upgrade patterns	funnel design (activation triggers)

---

🔬 Profundizar con Deep Research

Al entregar, añade:

📊 **¿Quieres profundizar?**
→ Escribe **"profundizar"** para continuar.

---

📁 Almacenamiento (OBLIGATORIO)

brand/{{slug}}/customer-data/
├── customer-data.current.md      ← versión activa
├── v1.md, v2.md... ← versiones anteriores
├── history.json    ← log de versiones
└── qa-log.md       ← historial de QA

1. Identifica slug desde systemPrompt
2. Si existe customer-data.current.md → backup como v{N+1}.md
3. Si no existe → crea carpeta + customer-data.current.md + v1.md + history.json
4. Link: <MC_BASE>/docs/brand/{slug}/customer-data/customer-data.current.md