Skill: samledger67-dotcom/subscription-revenue-tracker (version 1.1.0)

--- name: subscription-revenue-tracker description: > SaaS and subscription business revenue intelligence. Track MRR/ARR, calculate churn rate, net revenue retention (NRR), customer lifetime value (LTV), cohort analysis, and payback periods. Connects to Stripe, Chargebee, or CSV exports for automated metric computation. Outputs investor-ready dashboards, board decks, and QBO journal entries for deferred revenue. Use when: building SaaS financial models, calculating subscription KPIs, preparing investor updates, analyzing cohort retention, or booking deferred revenue correctly in the GL. NOT for: one-time transaction businesses, ecommerce without subscriptions, crypto revenue (use defi-position-tracker), QBO data entry (use qbo-automation), or payroll processing. metadata: category: finance tags: [saas, mrr, arr, churn, retention, cohort, stripe, subscription, ltv, revenue] requires: optional_bins: [stripe, python3, node, jq, curl] apis: [Stripe API, Chargebee API, QuickBooks Online API] --- # Subscription Revenue Tracker Track MRR/ARR, churn, NRR, cohort retention, and LTV for SaaS and subscription businesses. Produces investor-grade metrics and clean GL entries. --- ## Core Metrics Defined | Metric | Formula | Why It Matters | |--------|---------|----------------| | MRR | Sum of all active recurring monthly revenue | Pulse of the business | | ARR | MRR × 12 | Annualized scale metric for investors | | New MRR | Revenue from new customers this month | Growth engine | | Expansion MRR | Upgrades / upsells from existing customers | Efficiency signal | | Contraction MRR | Downgrades from existing customers | Negative signal | | Churned MRR | Revenue lost from cancellations | Retention health | | Net New MRR | New + Expansion − Contraction − Churned | Net growth | | Gross Churn Rate | Churned MRR / Beginning MRR | Revenue decay rate | | Net Revenue Retention (NRR) | (Beginning + Expansion − Contraction − Churned) / Beginning | Growth from existing base | | LTV | ARPU / Gross Churn Rate | Customer economic value | | CAC | Sales + Marketing Spend / New Customers | Acquisition cost | | LTV:CAC | LTV / CAC | Unit economics health (target: >3x) | | Payback Period | CAC / (ARPU × Gross Margin) | Months to recover acquisition cost | --- ## Workflows ### 1. Pull MRR from Stripe ```bash # List all active subscriptions with their amounts stripe subscriptions list \ --status=active \ --limit=100 \ --expand[]=data.items.data \ 2>&1 | jq ' .data[] | { id: .id, customer: .customer, status: .status, current_period_start: (.current_period_start | strftime("%Y-%m-%d")), mrr: (.items.data[0].price.unit_amount / 100 * (if .items.data[0].price.recurring.interval == "year" then 1/12 else 1 end)) } ' ``` **Get MRR summary via Stripe API (no CLI):** ```bash curl "https://api.stripe.com/v1/subscriptions?status=active&limit=100&expand[]=data.items.data" \ -u sk_live_YOUR_KEY: | jq ' [.data[] | (.items.data[0].price.unit_amount / 100) * (if .items.data[0].price.recurring.interval == "year" then 1/12 else 1 end) ] | add ' ``` **Python: Full MRR waterfall from Stripe events:** ```python import stripe from datetime import datetime, timezone from collections import defaultdict from dateutil.relativedelta import relativedelta stripe.[REDACTED_SECRET]" def get_mrr_waterfall(year: int, month: int) -> dict: """ Calculate MRR waterfall for a given month. Returns: new, expansion, contraction, churned, net_new MRR. """ # Period boundaries period_start = datetime(year, month, 1, tzinfo=timezone.utc) period_end = period_start + relativedelta(months=1) prev_start = period_start - relativedelta(months=1) # Get subscriptions active at start of period (denominator) beginning_subs = _get_active_subscriptions_at(prev_start) ending_subs = _get_active_subscriptions_at(period_end) # Categorize by customer beginning_customers = {s.customer: _get_mrr(s) for s in beginning_subs} ending_customers = {s.customer: _get_mrr(s) for s in ending_subs} new_mrr = 0.0 expansion_mrr = 0.0 contraction_mrr = 0.0 churned_mrr = 0.0 all_customers = set(beginning_customers) | set(ending_customers) for cust_id in all_customers: begin_val = beginning_customers.get(cust_id, 0.0) end_val = ending_customers.get(cust_id, 0.0) delta = end_val - begin_val if begin_val == 0 and end_val > 0: new_mrr += end_val elif begin_val > 0 and end_val == 0: churned_mrr += begin_val elif delta > 0: expansion_mrr += delta elif delta < 0: contraction_mrr += abs(delta) beginning_mrr = sum(beginning_customers.values()) return { "period": f"{year}-{month:02d}", "beginning_mrr": beginning_mrr, "new_mrr": new_mrr, "expansion_mrr": expansion_mrr, "contraction_mrr": contraction_mrr, "churned_mrr": churned_mrr, "net_new_mrr": new_mrr + expansion_mrr - contraction_mrr - churned_mrr, "ending_mrr": beginning_mrr + new_mrr + expansion_mrr - contraction_mrr - churned_mrr, "gross_churn_rate": churned_mrr / beginning_mrr if beginning_mrr else 0, "nrr": (beginning_mrr + expansion_mrr - contraction_mrr - churned_mrr) / beginning_mrr if beginning_mrr else 0, } def _get_mrr(subscription) -> float: """Extract normalized monthly value from a Stripe subscription.""" item = subscription.get("items", {}).get("data", [{}])[0] price = item.get("price", {}) amount = price.get("unit_amount", 0) / 100 qty = item.get("quantity", 1) interval = price.get("recurring", {}).get("interval", "month") if interval == "year": return (amount * qty) / 12 elif interval == "week": return (amount * qty) * 4.333 return amount * qty def _get_active_subscriptions_at(timestamp: datetime) -> list: """Get subscriptions that were active at a given timestamp.""" ts = int(timestamp.timestamp()) subs = stripe.Subscription.list( status="all", created={"lte": ts}, limit=100 ) return [ s for s in subs.auto_paging_iter() if s.current_period_start <= ts <= (s.canceled_at or ts + 1) ] ``` ### 2. Cohort Analysis Track retention by signup cohort — the gold standard for understanding retention quality: ```python import pandas as pd import numpy as np def build_cohort_table(subscription_events: pd.DataFrame) -> pd.DataFrame: """ Build monthly cohort retention table. Input columns: customer_id, event_type (started/churned), event_month (YYYY-MM) Output: matrix of cohort × months_since_start → retention percentage Example output: cohort | M+0 | M+1 | M+2 | M+3 | M+6 | M+12 2025-01 | 100% | 87% | 79% | 74% | 65% | 54% 2025-02 | 100% | 91% | 83% | 78% | -- | -- """ # Assign cohort (month of first subscription) first_sub = (subscription_events[subscription_events.event_type == "started"] .groupby("customer_id")["event_month"] .min() .reset_index() .rename(columns={"event_month": "cohort"})) df = subscription_events.merge(first_sub, on="customer_id") df["cohort"] = pd.to_datetime(df["cohort"]) df["event_month"] = pd.to_datetime(df["event_month"]) df["months_since_start"] = ( (df["event_month"].dt.year - df["cohort"].dt.year) * 12 + (df["event_month"].dt.month - df["cohort"].dt.month) ) # Active customers per cohort per month active = (df[df.event_type != "churned"] .groupby(["cohort", "months_since_start"])["customer_id"] .nunique() .reset_index() .rename(columns={"customer_id": "active_customers"})) cohort_table = active.pivot( index="cohort", columns="months_since_start", values="active_customers" ) # Normalize to cohort size (M+0 = 100%) cohort_sizes = cohort_table[0] retention_table = cohort_table.divide(cohort_sizes, axis=0) * 100 return retention_table.round(1) def average_retention_curve(cohort_table: pd.DataFrame, min_cohorts: int = 3) -> pd.Series: """ Compute average retention curve across cohorts with enough data. Used for LTV projection. """ # Only include cohorts with at least min_cohorts data points per period valid_cols = cohort_table.columns[cohort_table.notna().sum() >= min_cohorts] return cohort_table[valid_cols].mean() ``` ### 3. LTV and Unit Economics ```python def calculate_ltv(arpu: float, gross_margin: float, monthly_churn_rate: float) -> dict: """ Calculate Customer Lifetime Value and payback metrics. Args: arpu: Average Revenue Per User per month gross_margin: Gross margin % (0.0-1.0) monthly_churn_rate: Monthly revenue churn rate (0.0-1.0) Returns: LTV, gross profit LTV, and key benchmarks """ if monthly_churn_rate <= 0: raise ValueError("Churn rate must be > 0 for LTV calculation") avg_customer_lifetime_months = 1 / monthly_churn_rate ltv_revenue = arpu * avg_customer_lifetime_months ltv_gross_profit = ltv_revenue * gross_margin return { "arpu_monthly": arpu, "arpu_annual": arpu * 12, "monthly_churn_rate": monthly_churn_rate, "avg_lifetime_months": avg_customer_lifetime_months, "ltv_revenue": ltv_revenue, "ltv_gross_profit": ltv_gross_profit, "benchmarks": { "saas_target_ltv_cac": ">3x", "saas_target_payback": "<12 months", "saas_target_nrr": ">100%", } } def payback_period(cac: float, arpu: float, gross_margin: float) -> dict: """ Calculate CAC payback period in months. Healthy SaaS: <12 months Great SaaS: <6 months Struggling: >18 months """ monthly_gross_profit_per_customer = arpu * gross_margin if monthly_gross_profit_per_customer <= 0: return {"payback_months": float("inf"), "status": "never — negative gross margin"} months = cac / monthly_gross_profit_per_customer if months < 6: status = "excellent" elif months < 12: status = "healthy" elif months < 18: status = "acceptable" else: status = "concerning" return { "cac": cac, "arpu_monthly": arpu, "gross_margin": gross_margin, "payback_months": round(months, 1), "payback_status": status, } ``` ### 4. CSV Import (Non-Stripe Businesses) For businesses without Stripe — import from any billing system: ```python # Expected CSV format: # customer_id, plan_name, mrr, start_date, end_date (blank if active), currency import pandas as pd from datetime import datetime def load_subscriptions_from_csv(path: str, as_of_date: str = None) -> pd.DataFrame: """ Load subscription data from a CSV export. Handles Chargebee, Recurly, Zuora, or manual exports. Required columns: customer_id, mrr, start_date Optional: end_date, plan_name, currency """ df = pd.read_csv(path) df["start_date"] = pd.to_datetime(df["start_date"]) if "end_date" in df.columns: df["end_date"] = pd.to_datetime(df["end_date"], errors="coerce") if as_of_date: cutoff = pd.Timestamp(as_of_date) # Active = started before cutoff AND (not ended OR ended after cutoff) df = df[ (df["start_date"] <= cutoff) & (df.get("end_date", pd.NaT).isna() | (df.get("end_date", pd.NaT) > cutoff)) ] # Standardize MRR (handle annual → monthly) if "billing_interval" in df.columns: df.loc[df.billing_interval == "annual", "mrr"] /= 12 return df def compute_metrics_from_csv(csv_path: str, period: str) -> dict: """ Full metrics computation from CSV for a given YYYY-MM period. """ year, month = map(int, period.split("-")) # Current and previous month active subs period_end = datetime(year, month, 28) # safe month-end prev_period_end = datetime(year, month - 1 if month > 1 else 12, 28) current = load_subscriptions_from_csv(csv_path, period_end.strftime("%Y-%m-%d")) previous = load_subscriptions_from_csv(csv_path, prev_period_end.strftime("%Y-%m-%d")) curr_by_cust = current.groupby("customer_id")["mrr"].sum() prev_by_cust = previous.groupby("customer_id")["mrr"].sum() new_customers = curr_by_cust.index.difference(prev_by_cust.index) churned_customers = prev_by_cust.index.difference(curr_by_cust.index) existing = curr_by_cust.index.intersection(prev_by_cust.index) expansion = (curr_by_cust[existing] - prev_by_cust[existing]).clip(lower=0).sum() contraction = (prev_by_cust[existing] - curr_by_cust[existing]).clip(lower=0).sum() beginning_mrr = prev_by_cust.sum() return { "period": period, "customer_count": len(curr_by_cust), "mrr": curr_by_cust.sum(), "arr": curr_by_cust.sum() * 12, "beginning_mrr": beginning_mrr, "new_mrr": curr_by_cust[new_customers].sum(), "expansion_mrr": expansion, "contraction_mrr": contraction, "churned_mrr": prev_by_cust[churned_customers].sum(), "net_new_mrr": curr_by_cust.sum() - beginning_mrr, "gross_churn_rate": prev_by_cust[churned_customers].sum() / beginning_mrr if beginning_mrr else 0, "nrr": (beginning_mrr + expansion - contraction - prev_by_cust[churned_customers].sum()) / beginning_mrr if beginning_mrr else 0, "arpu": curr_by_cust.mean(), } ``` ### 5. Deferred Revenue GL Entries (QBO-Ready) Subscription revenue must be recognized over the service period (ASC 606 / IFRS 15): ```python from datetime import date from dateutil.relativedelta import relativedelta def deferred_revenue_schedule( invoice_date: date, invoice_amount: float, service_start: date, service_end: date, description: str ) -> list[dict]: """ Generate monthly revenue recognition journal entries for an annual subscription. At invoice: Debit A/R, Credit Deferred Revenue Monthly: Debit Deferred Revenue, Credit Revenue Returns list of journal entries ready for QBO import. """ total_days = (service_end - service_start).days entries = [] # Initial: recognize deferred revenue liability entries.append({ "date": invoice_date.isoformat(), "type": "invoice_booking", "description": f"Book deferred revenue — {description}", "debit_account": "Accounts Receivable", "credit_account": "Deferred Revenue", "amount": invoice_amount, }) # Monthly recognition current_date = date(service_start.year, service_start.month, 1) while current_date <= service_end: # Days in this period period_end = min( date(current_date.year, current_date.month + 1, 1) - relativedelta(days=1), service_end ) period_days = (period_end - max(current_date, service_start)).days + 1 period_revenue = invoice_amount * (period_days / total_days) entries.append({ "date": current_date.isoformat(), "type": "revenue_recognition", "description": f"Revenue recognition — {description} ({current_date.strftime('%b %Y')})", "debit_account": "Deferred Revenue", "credit_account": "Subscription Revenue", "amount": round(period_revenue, 2), "period_days": period_days, }) current_date = date(current_date.year, current_date.month, 1) + relativedelta(months=1) return entries # Example: $12,000 annual subscription entries = deferred_revenue_schedule( invoice_date=date(2026, 1, 1), invoice_amount=12000.00, service_start=date(2026, 1, 1), service_end=date(2026, 12, 31), description="Acme Corp — Enterprise Plan" ) # → 13 entries: 1 booking + 12 monthly recognition of $1,000 each ``` ### 6. Investor-Ready Output ```python def generate_investor_summary(metrics_history: list[dict]) -> dict: """ Generate board/investor MRR summary from 12 months of metrics. Returns formatted dict suitable for pitch deck tables or Sheets export. """ if len(metrics_history) < 2: raise ValueError("Need at least 2 months of data for growth calculations") latest = metrics_history[-1] prev_month = metrics_history[-2] twelve_months_ago = metrics_history[0] if len(metrics_history) >= 12 else None mom_growth = (latest["mrr"] - prev_month["mrr"]) / prev_month["mrr"] if prev_month["mrr"] else 0 yoy_growth = None if twelve_months_ago and twelve_months_ago["mrr"]: yoy_growth = (latest["mrr"] - twelve_months_ago["mrr"]) / twelve_months_ago["mrr"] # Rule of 40: YoY Revenue Growth % + EBITDA Margin % >= 40 is healthy SaaS # (requires EBITDA margin input from financial model) return { "as_of": latest["period"], "mrr": f"${latest['mrr']:,.0f}", "arr": f"${latest['arr']:,.0f}", "mrr_mom_growth": f"{mom_growth:.1%}", "mrr_yoy_growth": f"{yoy_growth:.1%}" if yoy_growth is not None else "N/A", "nrr": f"{latest['nrr']:.1%}", "gross_churn": f"{latest['gross_churn_rate']:.2%}", "customer_count": latest["customer_count"], "arpu": f"${latest['arpu']:,.0f}", "new_mrr": f"${latest['new_mrr']:,.0f}", "expansion_mrr": f"${latest['expansion_mrr']:,.0f}", "churned_mrr": f"${latest['churned_mrr']:,.0f}", "net_new_mrr": f"${latest['net_new_mrr']:,.0f}", "benchmarks": { "nrr_status": "elite" if latest["nrr"] > 1.20 else "strong" if latest["nrr"] > 1.10 else "healthy" if latest["nrr"] > 1.00 else "concerning", "churn_status": "excellent" if latest["gross_churn_rate"] < 0.01 else "healthy" if latest["gross_churn_rate"] < 0.02 else "high", } } ``` --- ## Benchmark Reference ### SaaS Health Benchmarks (2025) | Metric | Best in Class | Healthy | Watch | |--------|--------------|---------|-------| | MoM MRR Growth | >15% | 5-15% | <5% | | Gross Churn (monthly) | <0.5% | 0.5-2% | >2% | | NRR | >120% | 100-120% | <100% | | LTV:CAC | >5x | 3-5x | <3x | | CAC Payback | <6 mo | 6-12 mo | >12 mo | | Rule of 40 | >40 | 20-40 | <20 | ### Revenue Recognition (ASC 606) - **Annual prepaid subscriptions:** Recognize monthly (1/12 per month) - **Multi-year contracts:** Recognize over full term - **Usage-based billing:** Recognize as consumed - **Setup fees:** Recognize ratably with subscription unless distinct performance obligation --- ## Integration with Other Skills - **startup-financial-model:** Feed MRR history and projections as revenue driver - **kpi-alert-system:** Alert when churn > threshold or NRR drops below 100% - **qbo-automation:** Import deferred revenue journal entries automatically - **investor-memo-generator:** Pull MRR waterfall data for Section 3 (Financial Performance) - **report-generator:** Generate monthly board report with cohort retention tables --- ## Not For This Skill - **Executing Stripe API write operations** (refunds, subscription changes) — use a billing management skill - **QBO data entry** — use qbo-automation after generating journal entries here - **Crypto/DeFi revenue** — use defi-position-tracker - **One-time product sales** without subscription component — use financial-analysis-agent - **Payroll or employee compensation** — use payroll tools - **Tax filing or 1099 generation** — use crypto-tax-agent or consult a CPA - **on-chain-payroll or qbo-to-tax-bridge** — PTIN-backed Moltlaunch services, not ClawHub


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