SaaS Metrics Dashboard Skill#

Calculate every critical SaaS metric from raw subscription data and produce an investor-ready health snapshot. This skill guides Sam Ledger through computing MRR/ARR, churn, retention, unit economics (LTV/CAC), growth efficiency, and Rule of 40 scoring — then formats the output for board decks, investor updates, or feeding into kpi-alert-system threshold monitoring.

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When to Use This Skill#

Trigger phrases:

• "What's our MRR / ARR?"
• "Calculate churn rate for last quarter"
• "Give me our LTV:CAC ratio"
• "Build a SaaS metrics snapshot"
• "Are we Rule of 40 compliant?"
• "Show net revenue retention"
• "Prepare metrics for the investor update"
• "What's our quick ratio / magic number?"
• "Track cohort retention"

Fits between:

• startup-financial-model — feeds historical actuals into the 3-statement model
• kpi-alert-system — outputs threshold-ready metric values for alert monitoring
• investor-memo-generator — supplies the metrics section of investor memos

NOT for:

• Businesses without recurring subscription revenue
• Real-time live dashboards (needs a BI tool like Metabase/Looker)
• Revenue recognition accounting (use revenue-recognition-agent)
• QBO bookkeeping entries (use qbo-automation)

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Core Metric Definitions#

Revenue Metrics#

MetricFormulaBenchmarkMRRSum of all monthly recurring charges—ARRMRR × 12—New MRRMRR from new customers this period—Expansion MRRUpsells + seat adds from existing customers—Churned MRRMRR lost from cancellations—Contraction MRRDowngrades from existing customers—Net New MRRNew + Expansion − Churned − ContractionPositive = growing

Retention Metrics#

MetricFormulaBenchmarkLogo ChurnCustomers churned / Customers at start<5%/yr for SMB, <2% enterpriseRevenue Churn (GRR)Churned+Contraction MRR / MRR at start>80% GRR goodNet Revenue Retention (NRR)(Start MRR + Expansion − Churn − Contraction) / Start MRR>110% excellent, >100% goodQuick Ratio(New + Expansion) / (Churned + Contraction)>4 = excellent, >2 = healthy

Unit Economics#

MetricFormulaBenchmarkLTVARPU / Monthly Churn Rate3× CAC minimumCAC(S&M Spend) / New Customers—LTV:CACLTV / CAC>3:1 healthy, >5:1 strongCAC PaybackCAC / (ARPU × Gross Margin%)<12mo excellent, <18mo goodMagic NumberNet New ARR / Prior Quarter S&M Spend>1.5 = efficient

Growth Efficiency#

MetricFormulaBenchmarkMoM Growth(MRR This Mo − Prior Mo) / Prior MoTop decile: 15%+ early stageRule of 40ARR Growth Rate % + FCF Margin %≥40 = healthyBurn MultipleNet Burn / Net New ARR<1 = excellent, <2 = good

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Data Inputs Required#

Minimum viable inputs:

text

Period: [month/quarter]
MRR at start of period: $___
New MRR: $___
Expansion MRR: $___
Churned MRR: $___
Contraction MRR: $___
New customers acquired: ___
Customers churned: ___
Total customers at start: ___
S&M spend (period): $___
Gross margin %: ___
ARR growth rate (YoY %): ___
FCF or operating cash flow: $___

Optional (for deeper analysis):

text

Cohort data (monthly signup + retention by cohort)
Plan-level MRR breakdown
Customer segment breakdown (SMB / Mid-Market / Enterprise)
NPS or CSAT scores

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Computation Walkthrough#

Step 1: Validate Inputs#

• Confirm all required fields are present
• Check: New MRR + Expansion − Churn − Contraction = Net New MRR
• Flag negative MRR values or impossible churn rates (>100%)

Step 2: Calculate Revenue Metrics#

python

net_new_mrr = new_mrr + expansion_mrr - churned_mrr - contraction_mrr
arr = (mrr_start + net_new_mrr) * 12

Step 3: Retention Metrics#

python

logo_churn = customers_churned / customers_start
grr = 1 - ((churned_mrr + contraction_mrr) / mrr_start)
nrr = (mrr_start + expansion_mrr - churned_mrr - contraction_mrr) / mrr_start
quick_ratio = (new_mrr + expansion_mrr) / (churned_mrr + contraction_mrr)

Step 4: Unit Economics#

python

arpu = (mrr_start + net_new_mrr) / (customers_start + new_customers - customers_churned)
ltv = arpu / (logo_churn / 12)  # annualized to monthly churn
cac = sm_spend / new_customers
ltv_cac = ltv / cac
cac_payback_months = cac / (arpu * gross_margin)
magic_number = (net_new_arr) / prior_sm_spend

Step 5: Growth Efficiency#

python

rule_of_40 = arr_growth_pct + fcf_margin_pct
burn_multiple = abs(net_burn) / net_new_arr  # only if burning cash

Step 6: Score & Grade#

Apply benchmark thresholds and assign health grades:

• 🟢 Green: At or above benchmark
• 🟡 Yellow: Within 20% of benchmark
• 🔴 Red: Below benchmark threshold

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Output Format#

Snapshot Output (Markdown/Structured)#

text

═══════════════════════════════════════
  SAAS METRICS SNAPSHOT — [Period]
  Company: [Name] | As of: [Date]
═══════════════════════════════════════

REVENUE
  MRR:              $___,___
  ARR:              $___,___
  Net New MRR:      $___,___  (New: $X | Exp: $X | Churn: -$X)
  MoM Growth:       X.X%  [🟢/🟡/🔴]

RETENTION
  NRR:              XXX%  [🟢/🟡/🔴]  (benchmark: >110%)
  GRR:              XX%   [🟢/🟡/🔴]  (benchmark: >80%)
  Logo Churn:       X.X%  [🟢/🟡/🔴]  (benchmark: <5%/yr)
  Quick Ratio:      X.X   [🟢/🟡/🔴]  (benchmark: >2)

UNIT ECONOMICS
  LTV:CAC:          X.Xx  [🟢/🟡/🔴]  (benchmark: >3:1)
  CAC Payback:      XX mo [🟢/🟡/🔴]  (benchmark: <18mo)
  Magic Number:     X.X   [🟢/🟡/🔴]  (benchmark: >1.5)

EFFICIENCY
  Rule of 40:       XX    [🟢/🟡/🔴]  (benchmark: ≥40)
  Burn Multiple:    X.X   [🟢/🟡/🔴]  (benchmark: <2)

OVERALL HEALTH:  🟢 Strong / 🟡 Watch / 🔴 At Risk
TOP RISKS:  [1-3 specific metrics to address]

CSV/JSON Output (for kpi-alert-system)#

When feeding downstream systems, output as:

json

{
  "period": "2026-Q1",
  "mrr": 125000,
  "arr": 1500000,
  "nrr": 118,
  "grr": 87,
  "logo_churn_annual": 4.2,
  "quick_ratio": 3.1,
  "ltv_cac": 4.5,
  "cac_payback_months": 11,
  "magic_number": 1.8,
  "rule_of_40": 52,
  "burn_multiple": 0.9
}

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Cohort Retention Analysis#

When cohort data is available, build a retention table:

text

Cohort  Mo1   Mo2   Mo3   Mo6   Mo12
Jan-25  100%  89%   82%   74%   68%
Feb-25  100%  91%   85%   77%   —
Mar-25  100%  88%   81%   —     —

Key outputs:

• Average Day-30 retention (target: >85% for SMB, >90% enterprise)
• 12-month retention (logo) — critical for LTV calculation
• Revenue cohort retention (expansion often offsets logo churn)

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Segment Benchmarks#

By Stage#

StageARRNRR TargetLogo ChurnRule of 40Pre-Seed<$1M>100%<10%N/ASeed$1-5M>105%<8%Growth-drivenSeries A$5-20M>110%<5%≥20Series B+$20M+>120%<3%≥40

By Customer Segment#

SegmentNRR TargetLogo ChurnCAC PaybackSMB>100%<10%/yr<6 monthsMid-Market>110%<5%/yr<12 monthsEnterprise>120%<2%/yr<18 months

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Integration with Other Skills#

→ startup-financial-model#

Pass current-period actuals as seed inputs for the 3-statement model:

• Use NRR to project expansion revenue
• Use logo churn to model customer count trajectory
• CAC payback informs S&M budget efficiency

→ kpi-alert-system#

Register thresholds for automated alerts:

text

NRR < 100%       → CRITICAL alert (negative net expansion)
Logo churn > 7%  → WARNING alert (annualized)
Quick ratio < 2  → WARNING alert
LTV:CAC < 3      → WARNING alert
Rule of 40 < 30  → INFO alert

→ investor-memo-generator#

Feed the snapshot JSON into the metrics section of investor memos. Include MoM/QoQ trends, not just point-in-time values.

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Examples#

Example 1: Quick Snapshot#

Input: "Our MRR is $125K. We added $18K new, $12K expansion, lost $8K churn, $3K contraction. 210 customers, lost 7. Spent $45K on S&M. 75% gross margin. YoY growth 85%. Burning $80K/mo."

Output: Full snapshot with grades, flagging Rule of 40 and burn multiple.

Example 2: Investor Prep#

Input: "Prepare Q1 2026 SaaS metrics for our Series A data room" Output: Formatted snapshot + cohort table + segment breakdown + narrative summary with investor framing.

Example 3: Board Deck Metrics Slide#

Input: "Give me the 6 most important metrics for our board deck" Output: MRR/ARR, NRR, Logo Churn, LTV:CAC, CAC Payback, Rule of 40 — each with prior period comparison and trend indicator.

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Red Flags to Surface#

Always flag proactively:

• NRR < 100% — losing more than you're gaining from existing customers
• Quick Ratio < 1 — churn eating new growth
• LTV:CAC < 2 — acquiring customers unprofitably
• CAC Payback > 24 months — severe capital efficiency problem
• Burn Multiple > 3 — burning too much per dollar of new ARR
• Logo churn > 15%/yr — product-market fit concern

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Precision. Integrity. Results. — PrecisionLedger