Revenue Forecasting Model

Build a data-driven revenue forecast:

**Company:** {company_name}
**Forecast Period:** {quarters} quarters
**Current MRR:** ${current_mrr}

**Historical Data:**
| Quarter | New MRR | Churn | Expansion | Net New MRR |
|---------|---------|-------|-----------|-------------|
| Q-4 | ${q4_new} | ${q4_churn} | ${q4_exp} | ${q4_net} |
| Q-3 | ${q3_new} | ${q3_churn} | ${q3_exp} | ${q3_net} |
| Q-2 | ${q2_new} | ${q2_churn} | ${q2_exp} | ${q2_net} |
| Q-1 | ${q1_new} | ${q1_churn} | ${q1_exp} | ${q1_net} |

**Cohort Analysis:**
- Month 1 retention: {m1_retention}%
- Month 6 retention: {m6_retention}%
- Month 12 retention: {m12_retention}%
- NRR: {nrr}%

**Forecast Assumptions:**
- New customer growth: {customer_growth}%/quarter
- Average ACV: ${avg_acv}
- Gross churn: {gross_churn}%/month
- Expansion rate: {expansion_rate}%/year

**Revenue Forecast:**
| Quarter | Beginning MRR | New MRR | Churn | Expansion | Ending MRR | Growth % |
|---------|---------------|---------|-------|-----------|------------|----------|
| Q1 | ${q1_begin} | ${q1_new_f} | ${q1_churn_f} | ${q1_exp_f} | ${q1_end} | {q1_growth}% |
| Q2 | ${q2_begin} | ${q2_new_f} | ${q2_churn_f} | ${q2_exp_f} | ${q2_end} | {q2_growth}% |
| Q3 | ${q3_begin} | ${q3_new_f} | ${q3_churn_f} | ${q3_exp_f} | ${q3_end} | {q3_growth}% |
| Q4 | ${q4_begin} | ${q4_new_f} | ${q4_churn_f} | ${q4_exp_f} | ${q4_end} | {q4_growth}% |

**Scenarios:**
- **Base:** ${base_arr}
- **Upside:** ${upside_arr} (if {upside_condition})
- **Downside:** ${downside_arr} (if {downside_condition})

**Key Drivers:**
1. **New Logos:** {new_logos}/quarter × ${avg_acv}
2. **Expansion:** {expansion_pct}% of existing base
3. **Churn:** {churn_pct}% monthly

**Sensitivity:**
Show how forecast changes with ±10% in each driver.

**Recommendation:**
Based on trends, forecast ${forecast_arr} ARR by end of period.
Confidence level: {confidence}%

Provide: Detailed forecast + scenarios + sensitivity analysis.