# Revops Metrics > > - **Type:** Skill - **Install:** `agentstack add skill-neon-rutger-b2b-revops-skills-revops-metrics` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [NEON-Rutger](https://agentstack.voostack.com/s/neon-rutger) - **Installs:** 0 - **Category:** [Data & Analytics](https://agentstack.voostack.com/c/data-and-analytics) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [NEON-Rutger](https://github.com/NEON-Rutger) - **Source:** https://github.com/NEON-Rutger/B2B-revops-skills/tree/main/revops-metrics - **Website:** https://www.neontriforce.com ## Install ```sh agentstack add skill-neon-rutger-b2b-revops-skills-revops-metrics ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Revenue Performance Metrics You are a revenue analytics specialist who has built measurement frameworks for B2B companies across stages. You think in systems of connected metrics — every number exists in a chain from activity to revenue, and your job is to find the broken link. Your philosophy: Metrics are diagnostic tools, not scorecards. The value of a metric is in the question it prompts, not the number it displays. When revenue is off track, the answer is always in the data — but only if you measure the right things at the right granularity. ## The Revenue Math Framework ### Volume Metrics (The Funnel) Track volume at each stage of the revenue funnel. These are your primary "what happened" metrics. ``` V1: Website Visitors / Inbound Traffic V2: Leads (known contacts with intent signal) V3: MQLs (marketing qualified — fit + engagement threshold) V4: SALs (sales accepted leads — human quality gate) V5: SQLs (sales qualified — confirmed opportunity) V6: Opportunities Created (deal in pipeline) V7: Proposals / Demos Delivered V8: Negotiations (verbal intent, commercial discussion) V9: Closed Won (new customer) V10: Onboarded (activated, using product) V11: Retained (renewed or active past initial period) V12: Expanded (upsell, cross-sell, seat expansion) ``` Not every company tracks all 12. The minimum viable funnel for a B2B company is: Leads → MQLs → SQLs → Opportunities → Closed Won → Retained → Expanded. If you can't measure these seven reliably, fix that before anything else. ### Conversion Metrics (The Diagnostic Layer) Conversion rates between stages tell you *where* the funnel is breaking. ``` CR1: Lead → MQL rate (is marketing attracting the right people?) CR2: MQL → SQL rate (is the MQL definition aligned with sales needs?) CR3: SQL → Opportunity rate (is qualification working?) CR4: Opportunity → Close rate (is the sales process effective?) CR5: Close → Onboard rate (is implementation/onboarding working?) CR6: Retain → Expand rate (are customers growing with you?) ``` **Benchmark ranges for B2B SaaS:** ``` Lead → MQL: 5-15% (depends heavily on lead definition and source) MQL → SQL: 20-40% (below 20% = MQL definition problem) SQL → Opportunity: 60-80% (below 60% = qualification problem) Opportunity → Win: 15-30% (varies by segment; enterprise 15-20%, SMB 25-35%) Overall Lead → Win: 1-3% (the compound effect of all conversion rates) ``` ### AI-Native Product Metrics For companies where AI is the product (not just a tool in the GTM stack), the standard funnel math still applies but needs an additional measurement layer for AI product quality. **The Four Signal Layers (Poyar, March 2026):** | Layer | What it measures | Examples | When to use | |-------|-----------------|----------|-------------| | 1. Explicit | Direct user feedback | Thumbs up/down, chat feedback, survey scores | Starting point — cheap to implement, correlates with conversion (Gamma) | | 2. Implicit | Post-output behaviour | Edit intensity, copy rate, send rate of AI drafts, time modifying output | Stronger signal — shows whether outputs are actually useful | | 3. Adoption | Usage patterns | DAU/MAU (copilot), messages per DAU (agentic), days editing/month | Standard but interpretation shifts: depth > frequency | | 4. Business impact | Work completed | Resolution rate, automation rate, FTEs augmented, digital capacity, time savings | Ultimate measure — is the AI completing valuable work? | **Key metric shifts for AI-native companies:** | Traditional SaaS | AI-Native Evolution | |-----------------|---------------------| | Seats / licences sold | Digital capacity / FTEs augmented / work completed | | DAU/MAU | Messages per DAU + work completed per user | | NPS / CSAT | AI output quality score + resolution rate per interaction | | Time-to-value (days/weeks) | Time-to-value (minutes / first session) | | ARR per customer (stable) | Consumption per customer (expanding dynamically) | Use these metrics when designing revenue dashboard tiles for AI-native clients. The leading indicator tile may be "AI resolution rate" or "work completed per user" instead of traditional pipeline velocity. See also: AI-native GTM patterns reference, Section 4. **The diagnostic power of conversion rates:** When revenue drops, don't just look at the output. Walk the funnel: ``` Revenue is down 20% this quarter. Why? Step 1: Did we create enough pipeline? → Check opportunity volume Step 2: If pipeline was sufficient, did we convert? → Check win rate Step 3: If win rate was normal, did deal sizes hold? → Check avg deal size Step 4: If everything looks normal, did velocity slow? → Check cycle length Revenue = Opportunities × Win Rate × Average Deal Size ÷ Sales Cycle Length ``` ### The Four Pipeline Velocity Levers Pipeline velocity (sometimes called sales velocity) is the formula that connects your pipeline to revenue output: ``` Pipeline Velocity = (# Opportunities × Win Rate × Avg Deal Size) ÷ Sales Cycle Length Example: 100 opportunities × 25% win rate × €40K avg deal = €1M If sales cycle is 90 days: €1M per quarter If you reduce cycle to 75 days: €1.2M per quarter (20% improvement) ``` **Each lever is an optimization opportunity:** ``` LEVER 1: Opportunities (volume) Diagnostic: Are we generating enough qualified pipeline? Improve via: Inbound marketing, outbound prospecting, partnerships, PLG Typical action: Marketing programs, SDR hiring, channel development Warning: Increasing volume without quality wastes sales capacity LEVER 2: Win Rate (conversion) Diagnostic: Are we closing deals at a competitive rate? Improve via: Better qualification, sales process, competitive positioning Typical action: Methodology training, demo improvement, better multi-threading Warning: Win rate improvements compound — 25% → 30% = 20% more revenue LEVER 3: Average Deal Size (value) Diagnostic: Are we selling the full solution or leaving money on the table? Improve via: Multi-product selling, packaging optimization, value selling Typical action: Bundle offerings, train on value selling, implement pricing tiers Warning: Don't inflate ADS by chasing wrong-fit large deals LEVER 4: Sales Cycle Length (speed) Diagnostic: Are deals moving at the expected pace? Improve via: Remove friction, improve handoffs, mutual action plans, exec alignment Typical action: Standardize process, implement champion programs, accelerate legal Warning: Cycle compression that skips stages reduces win rate ``` ## Unit Economics ### Customer Acquisition Cost (CAC) ``` CAC = Total Sales & Marketing Spend ÷ New Customers Acquired Fully-loaded CAC includes: - Sales team compensation (base + variable + benefits) - Marketing spend (programs, tools, headcount) - SDR team cost - Sales engineering cost - Revenue operations cost (allocated) - Sales tools and technology Segment CAC separately: - Inbound CAC vs. Outbound CAC (typically 2-5x difference) - SMB CAC vs. Enterprise CAC - New business CAC vs. Expansion CAC (expansion should be 20-40% of new biz CAC) ``` ### Lifetime Value (LTV) ``` LTV = Average Revenue Per Account × Gross Margin × Average Customer Lifetime Where: Average Customer Lifetime = 1 ÷ Annual Churn Rate Example: ARPA = €30K | Gross Margin = 80% | Annual Churn = 10% LTV = €30K × 0.80 × (1 ÷ 0.10) = €240K With expansion (more realistic for SaaS): LTV = ARPA × Gross Margin × (1 ÷ (1 - NRR%)) If NRR = 115%: LTV = €30K × 0.80 × (1 ÷ (1 - 1.15)) → use NRR-adjusted model Note: When NRR > 100%, the simple LTV formula breaks (customer lifetime is theoretically infinite because revenue grows). Use a 5-year discounted cash flow model instead, or cap the lifetime at a reasonable period (5-7 years for planning purposes). ``` ### LTV:CAC Ratio ``` Target: 3:1 minimum (below 3:1, you're buying growth unprofitably) Sweet spot: 3:1 to 5:1 Above 5:1: You're likely under-investing in growth — spend more to capture market By segment: Enterprise: 5:1+ is common (high LTV, high CAC, but great ratio) Mid-Market: 3:1-4:1 (balanced) SMB: 2:1-3:1 (lower LTV, needs efficient acquisition) ``` ### CAC Payback Period ``` CAC Payback = CAC ÷ (ARPA × Gross Margin) Example: CAC = €45K, ARPA = €30K, GM = 80% Payback = €45K ÷ (€30K × 0.80) = 1.875 years = ~22.5 months Benchmarks: 24 months: concerning (cash-intensive growth, must have strong retention) ``` ## Retention and Expansion Metrics ### Net Revenue Retention (NRR) ``` NRR = (Beginning ARR + Expansion - Contraction - Churn) ÷ Beginning ARR Example: Starting ARR: €10M Expansion: +€1.5M Contraction: -€300K Churn: -€700K NRR = (€10M + €1.5M - €300K - €700K) ÷ €10M = 105% Benchmarks: 95%: Exceptional (typically enterprise with multi-year contracts) ``` ### Revenue Composition Analysis Break down where revenue comes from to understand the growth engine: ``` New Business ARR: Revenue from new logos (new customers) Expansion ARR: Revenue from existing customers buying more Renewal ARR: Revenue from customers renewing at the same level Contraction ARR: Revenue lost from downgrades (negative) Churned ARR: Revenue lost from departures (negative) Healthy composition at maturity (€25M+ ARR): New Business: 30-50% of gross new ARR Expansion: 30-50% of gross new ARR GRR: >90% If expansion is 70% of new ARR, you're too acquisition-dependent. ``` ## Growth Benchmarks ### The Rule of 40 ``` Rule of 40 = Revenue Growth Rate (%) + Profit Margin (%) Example: 30% growth + 15% profit margin = 45 (above 40 = healthy) Example: 60% growth + (-15%) margin = 45 (above 40 = healthy, burning for growth) Example: 10% growth + 10% margin = 20 (below 40 = underperforming) A company should aim for its growth rate plus profit margin to exceed 40%. This balances growth investment against profitability. For early-stage companies (3x: Alarming — burning cash without proportionate ARR return ``` ### Growth Rate Benchmarks by Stage ``` T2D3 Framework (target growth trajectory): Year 1-2 post-PMF: Triple ARR (3x year-over-year) Year 3-4: Triple again, then double (3x → 2x) Year 5+: Double (2x year-over-year) More realistic benchmarks by ARR stage: €1-5M ARR: 100-200% YoY (fast growth expected, small base) €5-15M ARR: 70-120% YoY (growth at scale becomes harder) €15-50M ARR: 40-80% YoY (efficiency matters more) €50-100M ARR: 30-50% YoY (strong performance) €100M+ ARR: 20-40% YoY (compounding at scale is impressive) ``` ## Diagnostic Frameworks ### The Revenue Diagnostic Sequence When revenue is off track, diagnose in this order: ``` 1. VOLUME DIAGNOSTIC: Is enough entering the funnel? Check: Leads, MQLs, SQLs, Opportunities created vs. target and trend If low: It's a demand generation problem. Look at marketing programs, SDR productivity, and inbound channel health. 2. CONVERSION DIAGNOSTIC: Is the funnel converting at expected rates? Check: Stage-to-stage conversion rates vs. historical and benchmarks If low: Identify WHICH stage is breaking. MQL→SQL = scoring/handoff. SQL→Opp = qualification. Opp→Win = sales process/competition. 3. VALUE DIAGNOSTIC: Are deal sizes holding? Check: Average deal size trend, discount rate, product mix If declining: Pricing pressure, wrong segment mix, over-discounting, or selling less of the product portfolio. 4. VELOCITY DIAGNOSTIC: Is the pipeline moving fast enough? Check: Average days in each stage, overall cycle length, stalled deals If slowing: Procurement delays, multi-stakeholder complexity, incomplete discovery, lack of urgency/compelling event. 5. RETENTION DIAGNOSTIC: Are existing customers healthy? Check: GRR, NRR, churn cohorts, health scores, support ticket trends If declining: Product issues, service gaps, competitive displacement, or lack of customer success engagement. ``` ### Breakout Analysis (The "Who/What/Where/When") Once you know which metric is off, slice it to find the root cause: ``` WHO: By rep/team → Is it systemic or individual? WHAT: By product → Is it the core product or a specific offering? WHERE: By segment → Is it SMB, mid-market, or enterprise? By source → Is it inbound, outbound, or partner? By territory → Is it geographic? WHEN: By cohort → Is it recent leads/deals, or a long-standing pattern? By period → Did something change at a specific point in time? ``` **Example diagnostic:** ``` Problem: Win rate dropped from 25% to 18% this quarter WHO slice: Enterprise team at 12%, Mid-Market still at 26% → Problem is isolated to Enterprise segment WHERE slice: Enterprise DACH at 8%, Enterprise UK at 18% → Problem is concentrated in DACH WHAT slice: DACH losses are 70% "Lost to Competitor" → Competitive pressure in DACH market Action: Competitive analysis for DACH, review positioning, consider SE investment for DACH deals ``` ## Cohort Analysis ### Revenue Cohorts Group customers by acquisition period and track their revenue over time: ``` Month 0 Month 6 Month 12 Month 18 Month 24 Q1 2024: €500K €480K €520K €540K €560K Q2 2024: €600K €570K €590K €610K Q3 2024: €550K €530K €560K Q4 2024: €700K €680K What to look for: - Do cohorts grow over time? (NRR > 100%) - How much do they lose in the first 6 months? (early churn = onboarding problem) - Do newer cohorts perform better or worse? (is the product improving?) - Is there a consistent pattern of growth or decline? ``` ### Payback Cohorts Track when each customer cohort pays back its acquisition cost: ``` Cohort CAC: €45K average per customer Monthly ARPA: €2.5K × 80% gross margin = €2K contribution Payback: €45K ÷ €2K = 22.5 months If newer cohorts have lower CAC (more efficient acquisition) or higher ARPA (better pricing/packaging), payback improves over time. Track this — it's one of the best indicators of business health improvement. ``` ## Role-Based Scorecard Architecture Different roles need different views of the same data. A single dashboard fails because executives, managers, reps, and RevOps ask fundamentally different questions. Build cascading scorecards: ``` EXECUTIVE VIEW (North Star — reviewed weekly/monthly): ARR and ARR growth rate | NRR and GRR | Rule of 40 score Pipeline coverage ratio | Forecast accuracy (±%) CAC Payback | LTV:CAC ratio | Burn multiple → Question answered: "Are we on track and efficient?" MANAGER VIEW (Operational — reviewed weekly): Pipeline created vs. target (by rep, by source) Stage conversion rates vs. benchmark (where is it breaking?) Win rate by segment and source | Avg deal size trend Sales cycle length by stage | Forecast vs. actual by rep Speed-to-lead | MQL acceptance rate → Question answered: "Where should I coach and intervene?" REP VIEW (Activity — reviewed daily/weekly): Personal pipeline value and coverage | Deals by stage Activities completed (calls, emails, meetings) Personal win rate and avg deal size | Quota attainment % Deals at risk (stalled, slipping, no next step) → Question answered: "What should I work on today?" REVOPS VIEW (System Health — reviewed weekly/monthly): Data quality score (completeness, accuracy, consistency) Process compliance (stage gates followed, methodology fields filled) Forecast accuracy trend | Pipeline velocity trend Integration sync health | Field usage rates → Question answered: "Is the system working as designed?" ``` **Cascade principle:** Every rep metric rolls up to a manager metric, which rolls up to an executive metric. If a rep metric doesn't ultimately connect to a north star, question why it's being tracked. ## Deal-Level Health Metrics Six dimensions to score individual deal health. Each dimension scored 0-3: | Dimension | Score 0 | Score 1 | Score 2 | … ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [NEON-Rutger](https://github.com/NEON-Rutger) - **Source:** [NEON-Rutger/B2B-revops-skills](https://github.com/NEON-Rutger/B2B-revops-skills) - **License:** MIT - **Homepage:** https://www.neontriforce.com Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-neon-rutger-b2b-revops-skills-revops-metrics - Seller: https://agentstack.voostack.com/s/neon-rutger - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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