Skill: 1kalin/afrexai-startup-metrics-engine (version 1.0.0)

--- name: afrexai-startup-metrics-engine model: default version: 1.0.0 description: > Complete startup metrics command center — from raw data to investor-ready dashboards. Covers every stage (pre-seed to Series B+), every model (SaaS, marketplace, consumer, hardware), with diagnostic frameworks, benchmark databases, and board-ready reporting. tags: [startup, metrics, saas, kpis, unit-economics, growth, fundraising, investor, dashboard, arr, mrr, churn, ltv, cac] --- # Startup Metrics Command Center Your complete system for tracking, diagnosing, and communicating startup health — not just formulas, but the *thinking* behind what to measure, when, and what to do when numbers go wrong. --- ## Phase 1: Metrics Architecture ### Step 1 — Identify Your Model & Stage Before tracking anything, classify yourself: **Business Model:** ```yaml model_type: saas: sub_type: # self-serve | sales-led | PLG | hybrid pricing: # per-seat | usage-based | flat | tiered contract: # monthly | annual | multi-year marketplace: type: # managed | unmanaged | SaaS-enabled unit: # GMV | take-rate | transaction consumer: type: # subscription | ad-supported | freemium | transactional engagement_model: # DAU/MAU | session-based | content hardware_plus_software: type: # device + subscription | IoT | embedded ``` **Stage (determines what matters):** | Stage | ARR Range | North Star Focus | Board Cares About | |-------|-----------|-------------------|-------------------| | Pre-seed | $0-$50K | Engagement + retention signal | Problem-solution fit evidence | | Seed | $50K-$500K | Cohort retention + early revenue | Product-market fit signals | | Series A | $500K-$3M | Growth efficiency + unit economics | LTV:CAC, NDR, growth rate | | Series B | $3M-$15M | Scalability + operating leverage | Rule of 40, magic number, burn multiple | | Growth | $15M+ | Capital efficiency + market share | Net margins, NRR, competitive moat | ### Step 2 — Build Your Metric Stack **Layer 1: Health Vitals (track daily)** ``` - Revenue: MRR, ARR, net new MRR - Growth: MoM growth rate, WoW for early stage - Retention: Logo churn rate, revenue churn rate - Cash: Monthly burn, runway in months ``` **Layer 2: Efficiency (track weekly)** ``` - Unit economics: CAC, LTV, LTV:CAC ratio, payback months - Sales: Pipeline coverage, win rate, sales cycle length - Product: Activation rate, feature adoption, NPS/CSAT - Team: Revenue per employee, quota attainment ``` **Layer 3: Strategic (track monthly)** ``` - NDR (Net Dollar Retention) - Burn multiple - Rule of 40 score - Magic number - Cohort analysis curves ``` --- ## Phase 2: The Complete Formula Reference ### Revenue Metrics ``` MRR = Σ(active_subscriptions × monthly_price) ARR = MRR × 12 Net New MRR = New MRR + Expansion MRR - Churned MRR - Contraction MRR MRR Components: new_mrr: First-time customer revenue this month expansion_mrr: Upsell + cross-sell from existing customers churned_mrr: Revenue lost from customers who left contraction_mrr: Revenue lost from downgrades (customer stayed) reactivation_mrr: Revenue from returning churned customers MoM Growth = (MRR_current - MRR_previous) / MRR_previous CMGR (Compound Monthly Growth Rate) = (MRR_end / MRR_start)^(1/months) - 1 ``` **Why CMGR > MoM:** Monthly growth is noisy. CMGR smooths 6-12 month periods for real trend. ### Unit Economics ``` CAC = Total_Sales_Marketing_Spend / New_Customers_Acquired - Include: salaries, commissions, tools, ads, events, content costs - Exclude: product/engineering, CS (post-sale) - Time-lag adjustment: match spend to cohort it generated (typically 1-3 month lag) Blended CAC vs Channel CAC: blended_cac = total_spend / total_new_customers channel_cac = channel_spend / channel_new_customers # Always track both — blended hides channel problems LTV = ARPU × Gross_Margin% × Average_Customer_Lifetime # Or: LTV = ARPU × Gross_Margin% × (1 / Monthly_Churn_Rate) # Cap at 5 years for conservative estimates LTV:CAC Ratio — THE ratio: > 5.0 → Under-investing in growth (spend more!) 3.0-5.0 → Excellent efficiency 1.5-3.0 → Healthy but watch payback period 1.0-1.5 → Marginal — fix churn or reduce CAC < 1.0 → Burning cash per customer — STOP and fix CAC Payback = CAC / (Monthly_ARPU × Gross_Margin%) < 6 months → Elite (PLG companies) 6-12 months → Great 12-18 months → Acceptable for enterprise > 18 months → Danger zone (unless >130% NDR) ``` ### Retention & Churn ``` Logo Churn Rate = Customers_Lost / Customers_Start_of_Period Revenue Churn Rate = MRR_Lost / MRR_Start_of_Period # Revenue churn > logo churn = losing big customers (very bad) # Revenue churn < logo churn = losing small customers (less bad) Net Dollar Retention (NDR) = (Starting_MRR + Expansion - Contraction - Churn) / Starting_MRR > 130% → World-class (Snowflake, Twilio territory) 110-130% → Excellent 100-110% → Good 90-100% → Acceptable but concerning < 90% → Leaky bucket — growth can't outrun churn Gross Dollar Retention (GDR) = (Starting_MRR - Contraction - Churn) / Starting_MRR # NDR without expansion — shows your floor > 90% → Sticky product 80-90% → Normal for SMB < 80% → Product or market problem ``` ### Growth Efficiency ``` Burn Multiple = Net_Burn / Net_New_ARR < 1.0 → Amazing (rare at early stage) 1.0-1.5 → Great 1.5-2.0 → Good 2.0-3.0 → Mediocre > 3.0 → Bad — inefficient growth Rule of 40 = Revenue_Growth_Rate% + Profit_Margin% > 40 → Healthy SaaS (IPO-ready) # Example: 60% growth + -20% margin = 40 ✓ # Example: 20% growth + 20% margin = 40 ✓ Magic Number = Net_New_ARR_This_Quarter / Sales_Marketing_Spend_Last_Quarter > 1.0 → Efficient, invest more in S&M 0.5-1.0 → OK, optimize before scaling < 0.5 → Inefficient — fix before spending more Hype Ratio = Valuation / ARR # Reality check on fundraising expectations # Median SaaS multiples: 6-12x ARR (varies by growth + retention) ``` ### Cash & Runway ``` Monthly Burn = Total_Monthly_Expenses - Total_Monthly_Revenue Gross Burn = Total_Monthly_Expenses (ignoring revenue) Net Burn = Gross_Burn - Revenue Runway = Cash_Balance / Monthly_Net_Burn > 18 months → Comfortable 12-18 months → Start planning next raise 6-12 months → Urgently fundraising < 6 months → Default alive or dead calculation needed Default Alive? = Can_Current_Growth_Rate_Make_Revenue > Expenses_Before_Cash_Runs_Out # Paul Graham's test — if growing, project the intersection ``` ### Sales Efficiency ``` Sales Cycle Length = Avg_Days(First_Touch → Closed_Won) Pipeline Coverage = Total_Pipeline_Value / Revenue_Target # Need 3-4x for predictable revenue Win Rate = Deals_Won / Total_Deals_in_Stage By stage: SQL→Opp (30-40%), Opp→Proposal (50-60%), Proposal→Close (60-70%) ACV (Annual Contract Value) = Total_Contract_Value / Contract_Years ASP (Average Selling Price) = Total_Revenue / Deals_Closed Quota Attainment = Actual_Bookings / Quota_Target # Healthy org: 60-70% of reps hitting quota Sales Efficiency = Net_New_ARR / Fully_Loaded_Sales_Cost > 1.0 → Scalable ``` --- ## Phase 3: Diagnostic Framework — PULSE Method When a metric is off, don't just report it — diagnose it. ### P — Pattern Recognition ``` Questions: - Is this a trend (3+ months) or a blip (1 month)? - Is it seasonal or structural? - Did it change gradually or suddenly? - Which cohorts/segments are affected? ``` ### U — Upstream Tracing ``` Every metric has upstream drivers. Trace back: Revenue declining? → ├── New MRR down? → Lead volume? → Conversion rate? → Channel performance? ├── Expansion down? → Upsell attempts? → Product adoption? → CSM activity? └── Churn up? → Which segment? → Voluntary vs involuntary? → Reasons? CAC increasing? → ├── Spend up? → Which channels? → CPM/CPC changes? ├── Volume same but cost up? → Market saturation? → Competition? └── Conversion down? → Funnel stage? → Lead quality? → Sales process? ``` ### L — Leverage Point ``` Find the highest-impact intervention: - Which single metric, if improved 10%, would cascade the most? - What's the cheapest/fastest fix vs highest-impact fix? - Score: Impact (1-5) × Feasibility (1-5) × Speed (1-5) ``` ### S — So-What Translation ``` Convert metric into business language: - "Churn increased 2%" → "We'll lose $X00K ARR this year at this rate" - "CAC payback is 18 months" → "Each new customer is cash-negative for 1.5 years" - "NDR is 95%" → "Even with zero new sales, we shrink 5% annually" ``` ### E — Experiment Design ```yaml diagnostic_experiment: hypothesis: "[Metric] is declining because [upstream cause]" test: "[Specific action] for [time period]" success_metric: "[Metric] improves by [X%] within [timeframe]" sample: "[Segment/cohort to test on]" kill_criteria: "Stop if [negative signal] within [days]" ``` --- ## Phase 4: Cohort Analysis — The Truth Machine Aggregate metrics lie. Cohorts tell the truth. ### Revenue Cohort Table ``` Track each monthly cohort's MRR over time: Month 0 Month 1 Month 3 Month 6 Month 12 Jan '25 $50K $48K $45K $42K $38K Feb '25 $55K $53K $50K $48K — Mar '25 $60K $58K $57K $56K — Apr '25 $45K $44K $43K — — Reading this: - Jan cohort retained 76% at month 12 → mediocre - Mar cohort retained 93% at month 3 → improving! What changed? - Apr cohort started smaller but retention looks good ``` ### Engagement Cohort (Non-Revenue Signal) ```yaml cohort_engagement: week_1_activation: # % completing key action within 7 days week_4_habit: # % using product 3+ days in week 4 month_3_retention: # % still active at 90 days # Leading indicators of revenue retention # If engagement drops, revenue follows 1-3 months later ``` ### Cohort Red Flags ``` 🚩 Each new cohort retains worse → product-market fit eroding 🚩 Large cohorts churn more → scaling quality issues 🚩 Specific channel cohorts churn fast → bad-fit leads 🚩 Expansion only in old cohorts → pricing/packaging problem ``` --- ## Phase 5: Board & Investor Reporting ### Monthly Investor Update Template ```yaml investor_update: subject: "[Company] — [Month] Update: [One-line headline]" # 1. TL;DR (3 bullets max) highlights: - "ARR: $X (+Y% MoM) — [context]" - "Key win: [biggest achievement]" - "Challenge: [biggest problem + what you're doing]" # 2. Key Metrics Table metrics: arr: {current: "", prior_month: "", delta: ""} mrr: {current: "", growth_mom: ""} customers: {total: "", new: "", churned: ""} ndr: "" burn_rate: "" runway_months: "" cash_balance: "" # 3. What Happened (5-7 bullets) wins: [] challenges: [] # 4. What's Next (3-5 bullets) next_month_priorities: [] # 5. Asks (be specific!) asks: - intro: "Looking for intro to [person/company] for [reason]" - advice: "Would love 15 min on [specific topic]" - hiring: "Seeking [role] — know anyone?" ``` ### Board Deck Metric Slides **Slide 1: Business Health Dashboard** ``` ARR: $___ MoM: ___% NDR: ___% Customers: ___ New: ___ Churned: ___ Runway: ___ months Burn Multiple: ___ Traffic light: 🟢 On track | 🟡 Watch | 🔴 Action needed ``` **Slide 2: Revenue Waterfall** ``` Starting MRR: $___ + New: $___ + Expansion: $___ - Contraction: $___ - Churn: $___ = Ending MRR: $___ ``` **Slide 3: Unit Economics** ``` CAC: $___ → LTV: $___ → LTV:CAC: ___x Payback: ___ months Blended vs top channel efficiency ``` --- ## Phase 6: Model-Specific Metrics ### SaaS Additions ``` Quick Ratio = (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR) > 4.0 → Very healthy growth 2.0-4.0 → Good 1.0-2.0 → Sustainable but slow < 1.0 → Shrinking Logo-to-Revenue Retention Gap: If logo retention 85% but revenue retention 95% → upsell compensates If logo retention 85% and revenue retention 85% → no expansion = problem Expansion Revenue % = Expansion MRR / Total New MRR > 30% → Healthy at scale # Best SaaS: expansion > new revenue (Twilio was 170% NDR) ``` ### Marketplace Additions ``` GMV (Gross Merchandise Value) = Total value of transactions on platform Take Rate = Platform Revenue / GMV 5-15% → Typical for most marketplaces 15-30% → Managed/full-service marketplaces Supply-side metrics: supply_liquidity = listings_with_transaction / total_listings time_to_first_match = avg_days_from_listing_to_sale Demand-side metrics: search_to_fill = completed_transactions / searches repeat_purchase_rate = returning_buyers / total_buyers ``` ### Consumer/PLG Additions ``` DAU/MAU Ratio: > 50% → Exceptional (messaging apps) 25-50% → Strong habit (social, productivity) 10-25% → Good (media, entertainment) < 10% → Weak engagement Viral Coefficient (K-factor) = Invites_per_User × Conversion_Rate > 1.0 → Viral growth (each user brings >1 new user) 0.5-1.0 → Amplified growth < 0.5 → Not viral — need paid acquisition Free-to-Paid Conversion: PLG benchmark: 2-5% of free users convert Freemium benchmark: 1-3% Enterprise self-serve: 5-15% Time to Value = Time from signup to "aha moment" # Reduce this aggressively — strongest lever for activation ``` --- ## Phase 7: Metric Manipulation Red Flags ### Vanity vs Real Metrics | Vanity (Avoid) | Real (Track) | |----------------|--------------| | Total signups | Activated users (completed key action) | | Page views | Engaged sessions (>2 min or action taken) | | "Pipeline" | Qualified pipeline (met ICP criteria) | | Gross revenue | Net revenue (after refunds + credits) | | Total customers | Active customers (logged in last 30d) | | Downloads | WAU/MAU | | "Partnerships" | Revenue from partnerships | ### Common Manipulation Tactics to Watch ``` 🚩 Counting annual contracts as MRR at signing (vs. monthly recognition) 🚩 Excluding "one-time" churns from churn rate 🚩 Using gross revenue instead of net 🚩 Measuring CAC without fully-loaded costs 🚩 Cherry-picking best cohort as "representative" 🚩 Counting reactivations as new customers 🚩 Using "committed ARR" (signed but not live) 🚩 Trailing-12-month NDR when recent cohorts are worse ``` --- ## Phase 8: Action Playbooks ### When CAC Is Too High ``` 1. Audit channel efficiency — kill bottom 20% channels 2. Improve activation rate (reduces wasted spend) 3. Increase conversion at each funnel stage (+10% each = compound effect) 4. Shift mix: more organic/PLG, less paid 5. Reduce sales cycle length (lower cost per deal) 6. Tighten ICP — stop selling to bad-fit customers ``` ### When Churn Is Too High ``` 1. Segment: which customers churn? (Size, channel, use case) 2. Time: when do they churn? (Month 1-3 = onboarding, 6-12 = value, 12+ = competition) 3. Reason: exit survey + CS interviews (top 3 reasons) 4. Fix activation if month 1-3 churn 5. Fix value delivery if month 6-12 churn 6. Fix switching cost / competitive moat if 12+ churn ``` ### When Growth Stalls ``` 1. Check: is TAM exhausted in current segment? → Expand to adjacent 2. Check: conversion rates declining? → Product or message fatigue 3. Check: CAC rising with flat volume? → Channel saturation 4. Check: expansion revenue flat? → Packaging/pricing problem 5. Check: sales cycle lengthening? → Market conditions or competition ``` ### When Raising Capital ``` Metrics investors care about BY STAGE: Pre-seed: Engagement, retention curves, market size Seed: MoM growth (15%+), retention cohorts, early unit economics Series A: $1M+ ARR, 3x+ YoY growth, LTV:CAC > 3, NDR > 100% Series B: $5M+ ARR, path to Rule of 40, burn multiple < 2, sales efficiency ``` --- ## Quick Commands - "Set up metrics for [stage] [model] startup" → Full metric stack recommendation - "Diagnose [metric]" → PULSE diagnostic framework - "Build investor update for [month]" → Template with guidance - "Cohort analysis on [data]" → Retention curve analysis - "Compare us to benchmarks" → Gap analysis vs stage-appropriate benchmarks - "What metrics for Series [A/B] raise?" → Investor-ready checklist - "Calculate unit economics from [data]" → Full LTV, CAC, payback analysis - "Red flag check" → Scan metrics for warning signs - "Board deck metrics" → Generate slide-ready metric views --- ## Edge Cases ### Multi-Product Companies Track metrics per product line AND blended. Watch for cross-subsidization where one product's margins mask another's losses. ### Usage-Based Pricing MRR is estimated, not contracted. Track committed vs consumed. Expansion is automatic (usage growth), so NDR is naturally higher — compare to usage-based peers, not seat-based. ### Negative Churn via Price Increases If NDR > 100% only because of price increases (not organic expansion), this is fragile. Separate price-driven vs usage-driven expansion. ### Very Early Stage (Pre-Revenue) Track leading indicators: activation rate, engagement frequency, NPS, waitlist growth, organic traffic, time-to-value. Revenue metrics come later — don't force them. ### Seasonal Businesses Use YoY comparisons, not MoM. Adjust cohort analysis for seasonal patterns. Build seasonal forecast models. --- *Built by AfrexAI — turning data into revenue.*


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