LTV por Canal Mapeado — SaaS B2B
Resumo Executivo
- 1
Funil longo B2B (trial → paid → expansion) sem visibilidade de LTV real por canal
- 2
Implementamos user_id unificado + BigQuery + cohort analysis + expansion tracking
- 3
Resultado: LTV mapeado por canal, realocação 18% budget para canais high-LTV
Métricas Antes/Depois
| KPI | Antes | Depois | Delta | Metodologia |
|---|---|---|---|---|
| Canais com LTV mapeado | 0% | 100% | Completo | BigQuery cohort analysis 12 meses |
| Budget reallocation | Uniform | 18% shift | +18pp | High-LTV channels vs low-LTV |
| Attribution accuracy | 34% | 89% | First-touch preserved through expansion | |
| Expansion revenue tracking | None | 100% | Implemented | Stripe webhooks → BigQuery |
Arquitetura Técnica
User ID Unification
Cross-platform user_id tracking (web + product)
BigQuery Integration
GA4 + Stripe + product events unified schema
Cohort Analysis
Custom BigQuery queries for LTV calculation
Attribution Preservation
First-touch attribution maintained through expansion
Revenue Tracking
Stripe webhooks for subscription + expansion events
Timeline do Projeto
1
Data Architecture
Semana 1-2- User ID strategy
- BigQuery schema design
- Data sources audit
2
Implementation
Semana 3-4- GA4 + product tracking
- Stripe integration
- Attribution logic
3
Analysis & Validation
Semana 5-6- Cohort analysis setup
- LTV calculations
- Data validation
4
Insights & Optimization
Semana 7-8- Channel LTV analysis
- Budget recommendations
- Dashboard setup
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