Customer Lifetime Value (CLV) Prediction

💰 ¿Qué es CLV?

Customer Lifetime Value es la predicción del valor monetario neto que un cliente aportará a la empresa durante toda su relación comercial.

Componentes del CLV

Historical Value: Valor real gastado hasta la fecha
Predicted Value: Valor estimado futuro
Total CLV: Historical + Predicted
Time Horizon: Período de predicción (1, 3, 5 años)

🧮 Algoritmo de Cálculo

Modelo Predictivo Híbrido

def calculate_clv(customer_data):
    # 1. CLV Histórico (baseline)
    historical_clv = sum(customer_orders.amount)
    
    # 2. Predicción basada en RFM
    rfm_multiplier = get_rfm_multiplier(rfm_segment)
    
    # 3. Predicción basada en tendencias
    trend_factor = calculate_trend_factor(purchase_history)
    
    # 4. Ajuste por churn probability
    churn_retention = 1 - churn_probability
    
    # 5. CLV Final
    predicted_clv = historical_clv * rfm_multiplier * trend_factor * churn_retention
    
    return {
        'historical': historical_clv,
        'predicted': predicted_clv,
        'total': historical_clv + predicted_clv,
        'confidence': calculate_confidence_score()
    }

Factores de RFM para CLV

RFM Segment	CLV Multiplier	Rationale
Champions	2.5x	Alta retención y gasto
Loyal Customers	2.0x	Comportamiento estable
Potential Loyalists	1.8x	Crecimiento esperado
Recent Customers	1.5x	Potencial por explorar
At Risk	0.8x	Probabilidad de churn
Lost	0.2x	Muy baja probabilidad de retorno

🎯 Tiers de CLV

Clasificación Automática

High Value (Tier 1)

CLV Total: > $10,000
Características: Champions o Loyal con alta frecuencia
Porcentaje: ~15% de la base
Contribución: 60-70% del revenue

Medium Value (Tier 2)

CLV Total: $2,000 - $10,000
Características: Mayoría de segmentos activos
Porcentaje: ~40% de la base
Contribución: 25-30% del revenue

Low Value (Tier 3)

CLV Total: < $2,000
Características: Nuevos clientes, At Risk, Lost
Porcentaje: ~45% de la base
Contribución: 5-15% del revenue

📊 Métricas de Ejemplo

Distribución CLV (Tenant 56)

{
  "overview": {
    "total_customers": 45282,
    "avg_clv": 15000.50,
    "total_clv": 679230000,
    "distribution": {
      "high": 6792,    // 15%
      "medium": 18113, // 40% 
      "low": 20377     // 45%
    }
  }
}

Top CLV Customers

Customer ID	Historical	Predicted	Total CLV	Tier	RFM Segment
12345	$25,000	$35,000	$60,000	High	Champions
67890	$18,000	$22,000	$40,000	High	Loyal
11111	$8,000	$12,000	$20,000	High	Champions
22222	$5,000	$3,000	$8,000	Medium	Potential Loyal

🚀 APIs de CLV

Obtener Análisis CLV

GET /api/v2/cdp/analytics/clv?tenant_id=56

Respuesta Completa

{
  "success": true,
  "data": {
    "overview": {
      "total_customers": 45282,
      "avg_clv": 15000.50,
      "total_clv": 679230000,
      "median_clv": 8500.00,
      "distribution": {
        "high": 6792,
        "medium": 18113,
        "low": 20377
      },
      "last_calculation": "2024-09-16T19:20:00Z"
    },
    "top_customers": [
      {
        "customer_id": 12345,
        "email": "vip@example.com",
        "historical_value": 25000,
        "predicted_value": 35000,
        "total_clv": 60000,
        "confidence_score": 0.85,
        "clv_tier": "high",
        "rfm_segment": "Champions",
        "total_orders": 25,
        "avg_order_value": 1000
      }
    ],
    "tier_breakdown": {
      "high": {
        "count": 6792,
        "percentage": 15.0,
        "avg_clv": 25000,
        "total_value": 169800000,
        "contribution": 60.2
      },
      "medium": {
        "count": 18113,
        "percentage": 40.0,
        "avg_clv": 5500,
        "total_value": 99621500,
        "contribution": 35.3
      },
      "low": {
        "count": 20377,
        "percentage": 45.0,
        "avg_clv": 500,
        "total_value": 10188500,
        "contribution": 4.5
      }
    }
  }
}

Filtrar por Tier

GET /api/v2/cdp/analytics/clv?tenant_id=56&tier=high&limit=100

CLV Individual

GET /api/v2/cdp/customers/12345/profile?tenant_id=56

Respuesta incluye sección CLV:

{
  "clv": {
    "historical_value": 25000,
    "predicted_value": 35000,
    "total_clv": 60000,
    "confidence_score": 0.85,
    "clv_tier": "high",
    "calculation_date": "2024-09-16T19:20:00Z",
    "factors": {
      "rfm_multiplier": 2.5,
      "trend_factor": 1.2,
      "churn_retention": 0.95
    }
  }
}

📈 Estrategias por Tier CLV

High Value Customers (Tier 1)

Marketing Strategy

Personal Account Managers: Atención 1:1
Exclusive Events: Early access, VIP experiences
Premium Support: 24/7, priority handling
Custom Offerings: Bespoke products, bulk discounts

Retention Focus

Satisfaction Surveys: Monthly check-ins
Loyalty Programs: Platinum tier benefits
Surprise & Delight: Unexpected gifts, upgrades
Risk Monitoring: Alert on behavioral changes

Medium Value Customers (Tier 2)

Growth Strategy

Upselling: Premium products, add-ons
Cross-selling: Related categories
Bundle Offers: Value packages
Referral Programs: Incentivized sharing

Engagement

Educational Content: Product guides, tips
Email Sequences: Nurture campaigns
Segmented Offers: Personalized promotions
Community Building: User groups, forums

Low Value Customers (Tier 3)

Optimization Strategy

Cost-Effective Channels: Email, social media
Automated Campaigns: Triggers, workflows
Entry-Level Products: Gateway offerings
Educational Content: Value demonstration

Conversion Focus

Onboarding: Smooth first experience
Quick Wins: Easy value demonstration
Social Proof: Reviews, testimonials
Gradual Upselling: Step-by-step progression

🎯 CLV-Based Campaign Examples

VIP Reactivation (High CLV + At Risk)

# Identificar High CLV customers que están At Risk
high_clv_at_risk = get_customers(
    clv_tier='high',
    rfm_segment='At Risk',
    days_since_last_order__gt=60
)

# Campaña especial de reactivación
for customer in high_clv_at_risk:
    send_vip_winback_campaign(
        customer_id=customer.id,
        discount_percent=30,
        personal_manager=True,
        exclusive_products=True
    )

Cross-Sell to Medium CLV

# Medium CLV customers con potential de upgrade
medium_clv_potential = get_customers(
    clv_tier='medium',
    rfm_segment__in=['Potential Loyalists', 'Recent Customers'],
    last_order_value__lt=avg_order_value
)

# Campaña de cross-selling inteligente
for customer in medium_clv_potential:
    recommended_products = get_intelligent_recommendations(
        customer_id=customer.id,
        strategy='clv_upgrade'
    )
    send_cross_sell_campaign(customer.id, recommended_products)

📊 KPIs de CLV

Métricas de Performance

CLV Growth Rate: +15% anual target
CLV/CAC Ratio: >3:1 objetivo
Tier Upgrade Rate: 10% Low→Medium, 5% Medium→High
Prediction Accuracy: >85% confidence

Benchmarks por Industria

Industria	Avg CLV	High Tier %	CLV/CAC
E-commerce Fashion	$2,500	12%	4:1
SaaS B2B	$15,000	20%	8:1
Retail Electronics	$1,800	8%	3:1
Subscription	$5,000	25%	12:1

Métricas de Segmento

-- CLV por RFM Segment
SELECT 
    rfm_segment,
    COUNT(*) as customers,
    AVG(total_clv) as avg_clv,
    SUM(total_clv) as total_value,
    AVG(confidence_score) as avg_confidence
FROM customer_clv_analysis 
WHERE tenant_id = 56
GROUP BY rfm_segment
ORDER BY avg_clv DESC;

🔮 Predictive Modeling

Features del Modelo

Historical Metrics: RFM values, order history
Behavioral Patterns: Seasonality, category preferences
Demographic Data: Age, location, device usage
Engagement Metrics: Email opens, site visits
External Factors: Economic indicators, competitors

Model Performance

# Métricas del modelo actual
model_metrics = {
    'accuracy': 0.87,
    'precision': 0.85,
    'recall': 0.89,
    'f1_score': 0.87,
    'mae': 1250.50,  # Mean Absolute Error en $
    'rmse': 2100.75  # Root Mean Square Error en $
}

Reentrenamiento

Frequency: Mensual con datos actualizados
Validation: A/B testing de predicciones
Monitoring: Drift detection automático
Feedback Loop: Resultados reales vs predicciones

🔄 Automatización CLV

Triggers Automáticos

# Recálculo semanal de CLV
schedule.every().sunday.at("02:00").do(recalculate_clv_all_tenants)

# Alertas de cambio de tier
if clv_tier_changed(customer_id):
    notify_account_manager(customer_id, new_tier)
    trigger_tier_change_campaign(customer_id)

# Campaigns automáticas basadas en CLV
if clv_tier == 'high' and churn_risk == 'high':
    escalate_to_retention_team(customer_id)
    send_emergency_retention_campaign(customer_id)

Integration con CRM

Salesforce: Sync CLV scores, tier updates
HubSpot: Automate lead scoring, campaign triggers
Intercom: Personalize support based on CLV
Klaviyo: Dynamic email content by CLV tier

💡 Tip: Utiliza CLV no solo para marketing, sino también para priorizar atención al cliente, desarrollo de productos y decisiones de inversión en adquisición.

💰 ¿Qué es CLV?​

Componentes del CLV​

🧮 Algoritmo de Cálculo​

Modelo Predictivo Híbrido​

Factores de RFM para CLV​

🎯 Tiers de CLV​

Clasificación Automática​

High Value (Tier 1)​

Medium Value (Tier 2)​

Low Value (Tier 3)​

📊 Métricas de Ejemplo​

Distribución CLV (Tenant 56)​

Top CLV Customers​

🚀 APIs de CLV​

Obtener Análisis CLV​

Respuesta Completa​

Filtrar por Tier​

CLV Individual​

📈 Estrategias por Tier CLV​

High Value Customers (Tier 1)​

Marketing Strategy​

Retention Focus​

Medium Value Customers (Tier 2)​

Growth Strategy​

Engagement​

Low Value Customers (Tier 3)​

Optimization Strategy​

Conversion Focus​

🎯 CLV-Based Campaign Examples​

VIP Reactivation (High CLV + At Risk)​

Cross-Sell to Medium CLV​

📊 KPIs de CLV​

Métricas de Performance​

Benchmarks por Industria​

Métricas de Segmento​

🔮 Predictive Modeling​

Features del Modelo​

Model Performance​

Reentrenamiento​

🔄 Automatización CLV​

Triggers Automáticos​

Integration con CRM​