# Revenue Operations > Revenue operations: pipeline analysis, forecast accuracy, GTM metrics (CAC, LTV, NRR), sales process design, territory planning, and RevOps dashboard design - Skill: `uitbreidenos/revenue-operations` (Agent Skill) - Install (CLI): `npx skillmds@latest add uitbreidenos/revenue-operations` - Raw SKILL.md: https://api.skillmd.com/api/skills/uitbreidenos/revenue-operations/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: UitbreidenOS (https://skillmd.com/u/uitbreidenos) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/uitbreidenos/revenue-operations --- # Revenue Operations Skill ## When to activate - Building a RevOps metrics framework (CAC, LTV, NRR, pipeline coverage) - Diagnosing why the sales forecast is inaccurate - Designing the sales process and stage definitions - Analysing pipeline health and identifying conversion bottlenecks - Setting up or auditing a CRM data model - Building a GTM metrics dashboard for leadership ## When NOT to use - Individual deal strategy — that's sales, not RevOps - Marketing campaign execution — use the email-automation or paid-ads skills - Customer success playbooks — use the customer-success skill - Product pricing decisions — use the pricing-strategy skill ## Instructions ### GTM metrics framework ``` Build a GTM metrics framework for [company]. Company type: [B2B SaaS / marketplace / services] Sales motion: [PLG / inside sales / field sales / partner-led] Stage: [pre-revenue / $0-1M ARR / $1-10M ARR / $10M+ ARR] GTM team: [founders selling / SDR + AE / full GTM team] Core GTM metrics by funnel stage: ACQUISITION: - MQLs generated: [marketing qualified leads per month] - MQL→SQL conversion rate: [%] (benchmark: 10-25% depending on ICP tightness) - CAC (Customer Acquisition Cost): [total sales + marketing spend / new customers] - CAC by channel: [paid / organic / partner / outbound] — identify most efficient channel - Time to first contact: [hours from MQL to SDR outreach] CONVERSION: - SQL→Opportunity conversion: [%] - Opportunity→Close-Won rate: [%] (benchmark: 15-25% for inside sales, 10-20% enterprise) - Average deal size: [ACV] - Sales cycle length: [days from opportunity created to close] - Win rate by segment: [SMB / mid-market / enterprise] — where do you win most? RETENTION AND EXPANSION: - NRR (Net Revenue Retention): [%] (benchmark: >100% is healthy, >120% is excellent) - GRR (Gross Revenue Retention): [%] — pure retention without expansion - Expansion ARR: [new ARR from existing customers per month] - Churn rate: [%] monthly or annual - Logo churn vs. revenue churn: [different stories] EFFICIENCY: - LTV:CAC ratio: [target >3x, >5x is strong] - CAC payback period: [months to recover acquisition cost] - Magic number: [ARR added / sales + marketing spend] (>0.75 is good) - Sales efficiency: [new ARR / quota-carrying headcount] Build the metrics framework with current benchmarks for my company type and stage. ``` ### Forecast accuracy ``` Diagnose and fix sales forecast accuracy for [team]. Current forecast accuracy: [X% accurate within [Y]% variance] Forecast method: [bottoms-up from reps / top-down from manager / AI-assisted] CRM: [Salesforce / HubSpot / Pipedrive / other] Sales cycle: [X days average] Forecast accuracy root causes: STAGE DEFINITION PROBLEMS (most common): - Stages are not based on buyer actions, only seller actions Fix: redefine stages as buyer milestones (e.g., "prospect sent technical evaluation document" not "AE had discovery call") - Missing exit criteria — reps can move deals forward without evidence Fix: add required fields per stage (e.g., decision maker identified Y/N, budget confirmed Y/N) REP OPTIMISM BIAS: - Reps inflate deal probability to avoid manager scrutiny Fix: use objective criteria to set probability, not rep gut feel Good signal: time in stage vs. average time in stage (stalled deal = lower probability) Better signal: buyer engagement score (email opens, meeting attendance, champion activity) SINGLE-THREADED DEALS: - Only one contact in the deal; they control all access to the buying committee Fix: flag single-threaded deals; require multi-threading as a stage exit criterion above SMB PIPELINE INSPECTION HYGIENE: □ CRM data completeness: close date, amount, stage, and decision maker required on every deal > $X □ Weekly pipeline review: deals that haven't moved in >14 days reviewed in 1:1 □ Deal stage audit: deals in late stages with no activity in 7 days = concern □ Oldest deals: anything > 2x the average sales cycle should be pushed or lost FORECAST CATEGORIES (Salesforce model): - Best Case: deals the rep is working hard but not fully committed - Commit: rep believes will close this period, will stake their credibility on it - Closed: already closed - Pipeline: too early or uncertain for this period Benchmark: Commit category should close at 80%+. If it's closing at <60%, the commit definition needs work. Build the forecast accuracy improvement plan for my team and CRM. ``` ### Pipeline analysis ``` Analyse pipeline health for [period]. Period: [current quarter / next quarter] Total pipeline: $[X] Quota: $[X] Pipeline coverage ratio: [pipeline / quota] Pipeline health framework: COVERAGE RATIO BENCHMARKS: - Inside sales (30-60 day cycle): 3-4x quota in pipeline - Enterprise (90-180 day cycle): 4-6x quota in pipeline - PLG (shorter cycle): 2-3x may be sufficient Your pipeline coverage: $[X pipeline] / $[X quota] = [Xx] Interpretation: [adequate / under-covered / over-covered] PIPELINE QUALITY ANALYSIS: Stage distribution (healthy pipeline has deals at every stage): | Stage | Deal count | Total value | % of pipeline | |---|---|---|---| | Prospecting | X | $X | X% | | Discovery | X | $X | X% | | Evaluation | X | $X | X% | | Proposal | X | $X | X% | | Negotiation | X | $X | X% | Red flags in distribution: - Too much pipeline in early stages: current quarter forecast is at risk - Too much pipeline in late stages: future quarters are thin ("pull-forward" risk) - Single large deal representing >30% of pipeline: binary outcome risk AGE ANALYSIS: Deals older than 1.5x average sales cycle in their stage = stalled Stalled deals by stage: | Stage | Average time expected | Deals past threshold | |---|---|---| DEAL SIZE CONCENTRATION: Top 3 deals as % of total pipeline: [X%] If top 3 deals = >40% of pipeline: concentration risk RECOMMENDED ACTIONS: □ Stalled deals: rep to review and either push forward with a specific next step or move to Closed-Lost □ Coverage gap: generate [X] new opportunities in next 30 days □ Single-threaded deals: multi-thread or accept the risk explicitly □ Large deal concentration: accelerate smaller deals to reduce dependency Generate the pipeline health report for my team's pipeline data. ``` ### RevOps dashboard ``` Design a RevOps dashboard for [audience]. Audience: [CEO / VP Sales / CRO / Board] Refresh rate: [real-time / daily / weekly] Data sources: [CRM / billing system / product analytics / marketing automation] CEO / Board dashboard (quarterly view): NORTH STAR: - ARR: $[X] (+X% QoQ) - NRR: [X%] - Burn multiple: [net burn / net new ARR] — efficiency signal GROWTH: - New ARR this quarter: $[X] vs. plan $[X] ([X]% attainment) - New logos: [X] vs. plan - Expansion ARR: $[X] - Churn ARR: ($[X]) EFFICIENCY: - CAC payback: [X] months - LTV:CAC: [Xx] - Magic number: [X] FORECAST: - Current quarter commit: $[X] (closes [X]% confidence) - Next quarter pipeline: $[X] coverage ratio: [Xx] VP SALES / CRO dashboard (weekly view): WEEK'S PERFORMANCE: - Deals closed this week: [X deals, $X ARR] - Deals expected to close by EOM: $[X] - Pipeline created this week: $[X] PIPELINE HEALTH: - Total pipeline by stage (bar chart) - Stalled deals (>1.5x avg time in stage): [X deals, $X] - Win rate trend (rolling 8 weeks) REP PERFORMANCE: - Quota attainment by rep (current period) - Pipeline coverage by rep (healthy: >3x) - Deals in commit by rep Design the dashboard for my specific audience with the right granularity and data sources. ``` ## Example **User:** We're $3M ARR, 12-person sales team, NRR is 94% and declining. CAC payback is 28 months. Leadership says "grow faster" but we're leaking customers. How do I frame the RevOps problem? **Claude's diagnosis:** The numbers tell a clear story that leadership may not be seeing: **you cannot grow faster without fixing retention first**. **The math:** - 94% NRR = 6% annual revenue erosion from the existing base - At $3M ARR, that's ~$180K/year going out the door before you sell a single new dollar - At 28-month CAC payback, every churned customer represents 28 months of acquisition cost wasted - If NRR is declining (was it 97% six months ago?), the leak is growing **Framing for leadership:** "We're currently filling a bucket with a hole in it. Doubling our sales team accelerates the inflow but the hole stays the same size. Fixing retention from 94% → 104% NRR generates more ARR impact than a 30% increase in new sales — and costs 5-10x less." **What 104% NRR means:** Your existing base grows revenue without any new sales. At $3M ARR with 104% NRR, you add $120K ARR/year from expansion alone. At 94%, you lose $180K. That's a $300K annual swing. **RevOps diagnosis to run:** 1. Churn cohort analysis: which customer segments churn most? (size? vertical? sales rep? channel?) 2. Time-to-churn: how long before they leave? (< 6 months = onboarding failure; 12-18 months = value delivery failure) 3. Exit interview data: do you have it? If not, start calling churned customers this week **Recommendation:** Before adding any sales headcount: hire 1 CS lead focused entirely on churn reduction. One CSM preventing 3 churns/quarter pays back in 2 months vs. 28 months for a new AE. ---