LoopyLuci SaaS Metrics and Reporting
Tracking, analyzing, and reporting SaaS business metrics — from recurring revenue and churn through unit economics, cohort analysis, and board reporting.
When to Use
- Building a SaaS metrics dashboard
- Preparing board decks and investor updates
- Analyzing subscription revenue and churn
- Calculating unit economics (LTV, CAC, payback)
- Running cohort analysis for retention
Core SaaS Metrics
from typing import Dict, List, Optional
from datetime import datetime, timedelta
from collections import defaultdict
class SaaS_Metrics:
"""Core SaaS metric calculations."""
@staticmethod
def calculate_mrr(subscriptions: List[Dict]) -> Dict:
"""Calculate Monthly Recurring Revenue."""
new_mrr = 0
churn_mrr = 0
expansion_mrr = 0
contraction_mrr = 0
for sub in subscriptions:
if sub.get('type') == 'new':
new_mrr += sub['amount']
elif sub.get('type') == 'churn':
churn_mrr += sub['amount']
elif sub.get('type') == 'upgrade':
expansion_mrr += sub.get('delta', sub['amount'])
elif sub.get('type') == 'downgrade':
contraction_mrr += abs(sub.get('delta', sub['amount']))
net_new_mrr = new_mrr + expansion_mrr - churn_mrr - contraction_mrr
return {
'new_mrr': new_mrr,
'churn_mrr': churn_mrr,
'expansion_mrr': expansion_mrr,
'contraction_mrr': contraction_mrr,
'net_new_mrr': net_new_mrr,
}
@staticmethod
def churn_rates(customers: List[Dict], period_days: int = 30) -> Dict:
"""Calculate logo and revenue churn."""
start_count = len(customers)
start_mrr = sum(c.get('mrr', 0) for c in customers)
churned = [c for c in customers if c.get('churned_in_period', False)]
churn_count = len(churned)
churn_mrr = sum(c.get('mrr', 0) for c in churned)
logo_churn = churn_count / max(start_count, 1) * 100
revenue_churn = churn_mrr / max(start_mrr, 1) * 100
return {
'logo_churn_rate': round(logo_churn, 2),
'revenue_churn_rate': round(revenue_churn, 2),
'logo_churn_count': churn_count,
'revenue_churn_amount': churn_mrr,
'annualized_logo_churn': round((1 - (1 - logo_churn/100) ** (365/period_days)) * 100, 2),
}
@staticmethod
def unit_economics(cac_data: Dict, ltv_data: Dict) -> Dict:
"""Calculate unit economics."""
cac = cac_data.get('total_sales_marketing_spend', 0) / max(cac_data.get('new_customers', 1), 1)
avg_revenue_per_user = ltv_data.get('avg_monthly_revenue', 0)
gross_margin = ltv_data.get('gross_margin_pct', 0.7)
churn_rate = ltv_data.get('monthly_churn_rate', 0.05)
# LTV = ARPU * Gross Margin / Monthly Churn
ltv = (avg_revenue_per_user * gross_margin) / max(churn_rate, 0.01)
ltv_cac = ltv / max(cac, 1)
# Payback period = CAC / (ARPU * Gross Margin)
monthly_contribution = avg_revenue_per_user * gross_margin
payback_months = cac / max(monthly_contribution, 1)
return {
'cac': round(cac, 2),
'ltv': round(ltv, 2),
'ltv_cac_ratio': round(ltv_cac, 2),
'payback_months': round(payback_months, 1),
'health': 'Excellent' if ltv_cac >= 5 else 'Good' if ltv_cac >= 3 else 'Needs improvement' if ltv_cac >= 1 else 'Unhealthy',
}
Cohort Analysis
class CohortAnalyzer:
"""Run retention cohort analysis."""
@staticmethod
def retention_cohorts(subscriptions: List[Dict]) -> Dict:
"""Calculate retention by monthly cohorts."""
cohorts = defaultdict(lambda: {'total': 0, 'periods': {}})
for sub in subscriptions:
cohort_key = sub.get('signup_month', 'unknown')
period = sub.get('month_since_signup', 0)
cohorts[cohort_key]['total'] += 1
if period not in cohorts[cohort_key]['periods']:
cohorts[cohort_key]['periods'][period] = 0
cohorts[cohort_key]['periods'][period] += 1
# Convert to retention percentages
cohort_table = {}
for month, data in sorted(cohorts.items()):
total = data['total']
rates = {}
for period in sorted(data['periods'].keys()):
rates[period] = round(data['periods'][period] / max(total, 1) * 100, 1)
cohort_table[month] = rates
return cohort_table
Board Deck Generator
def generate_board_slide(metrics: Dict) -> str:
"""Generate a board update slide for a key metric."""
slide = "📊 KPI: " + metrics.get('title', 'Metric') + "\n"
slide += "=" * 40 + "\n"
slide += f"Current: {metrics.get('current', 'N/A')}\n"
slide += f"Previous: {metrics.get('previous', 'N/A')}\n"
change = metrics.get('change_pct', 0)
arrow = "▲" if change > 0 else "▼" if change < 0 else "→"
slide += f"Change: {arrow} {abs(change)}%\n"
slide += f"Target: {metrics.get('target', 'N/A')}\n"
slide += f"Status: {'✅ On track' if metrics.get('on_track', False) else '⚠️ Needs attention'}\n"
slide += f"\n{metrics.get('commentary', '')}"
return slide
Common Pitfalls
- Ignoring contraction MRR — expansions hide downgrades; track both
- Gross vs net revenue retention — gross retention is what matters; net can be misleading
- Cohort analysis not segmented — aggregate retention hides different behaviors by segment
- CAC payback period too long — >18 months payback is risky for VC-backed SaaS
- nrr vs grr confusion — Net Revenue Retention includes upsells; Gross does not
- Not using SaaS benchmarks — a metric in isolation is meaningless; compare by stage and industry
Verification Checklist
- MRR tracked (new, churn, expansion, contraction)
- Logo and revenue churn rates calculated
- LTV and CAC calculated with clear definitions
- LTV:CAC ratio ≥ 3
- Monthly cohort retention analyzed
- Board deck template with standard SaaS metrics
- SaaS benchmarks comparison
- Dashboard automated (not manual spreadsheets)
See Also
- business-metrics-kpis — general business KPI framework
- crm-sales-pipeline — pipeline-to-revenue metrics
- customer-success-retention — churn reduction strategies
- digital-marketing-strategy — CAC optimization