- Add CSS components: .page-meta badges, .domain-header, .install-banner - Fix invisible tab navigation (explicit color for light/dark modes) - Rewrite generate-docs.py with design system templates - Domain indexes: centered headers with icons, install banners, grid cards - Skill pages: pill badges (domain, skill ID, source), install commands - Agent/command pages: type badges with domain icons - Regenerate all 210 pages (180 skills + 15 agents + 15 commands) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
186 lines
8.5 KiB
Markdown
186 lines
8.5 KiB
Markdown
---
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title: "CRO Advisor"
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description: "CRO Advisor - Claude Code skill from the C-Level Advisory domain."
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---
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# CRO Advisor
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<div class="page-meta" markdown>
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<span class="meta-badge">:material-account-tie: C-Level Advisory</span>
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<span class="meta-badge">:material-identifier: `cro-advisor`</span>
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<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/c-level-advisor/cro-advisor/SKILL.md">Source</a></span>
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</div>
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<div class="install-banner" markdown>
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<span class="install-label">Install:</span> <code>claude /plugin install c-level-skills</code>
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</div>
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Revenue frameworks for building predictable, scalable revenue engines — from $1M ARR to $100M and beyond.
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## Keywords
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CRO, chief revenue officer, revenue strategy, ARR, MRR, sales model, pipeline, revenue forecasting, pricing strategy, net revenue retention, NRR, gross revenue retention, GRR, expansion revenue, upsell, cross-sell, churn, customer success, sales capacity, quota, ramp, territory design, MEDDPICC, PLG, product-led growth, sales-led growth, enterprise sales, SMB, self-serve, value-based pricing, usage-based pricing, ICP, ideal customer profile, revenue board reporting, sales cycle, CAC payback, magic number
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## Quick Start
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### Revenue Forecasting
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```bash
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python scripts/revenue_forecast_model.py
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```
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Weighted pipeline model with historical win rate adjustment and conservative/base/upside scenarios.
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### Churn & Retention Analysis
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```bash
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python scripts/churn_analyzer.py
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```
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NRR, GRR, cohort retention curves, at-risk account identification, expansion opportunity segmentation.
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## Diagnostic Questions
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Ask these before any framework:
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**Revenue Health**
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- What's your NRR? If below 100%, everything else is a leaky bucket.
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- What percentage of ARR comes from expansion vs. new logo?
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- What's your GRR (retention floor without expansion)?
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**Pipeline & Forecasting**
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- What's your pipeline coverage ratio (pipeline ÷ quota)? Under 3x is a problem.
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- Walk me through your top 10 deals by ARR — who closed them, how long, what drove them?
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- What's your stage-by-stage conversion rate? Where do deals die?
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**Sales Team**
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- What % of your sales team hit quota last quarter?
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- What's average ramp time before a new AE is quota-attaining?
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- What's the sales cycle variance by segment? High variance = unpredictable forecasts.
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**Pricing**
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- How do customers articulate the value they get? What outcome do you deliver?
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- When did you last raise prices? What happened to win rate?
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- If fewer than 20% of prospects push back on price, you're underpriced.
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## Core Responsibilities (Overview)
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| Area | What the CRO Owns | Reference |
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|------|------------------|-----------|
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| **Revenue Forecasting** | Bottoms-up pipeline model, scenario planning, board forecast | `revenue_forecast_model.py` |
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| **Sales Model** | PLG vs. sales-led vs. hybrid, team structure, stage definitions | `references/sales_playbook.md` |
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| **Pricing Strategy** | Value-based pricing, packaging, competitive positioning, price increases | `references/pricing_strategy.md` |
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| **NRR & Retention** | Expansion revenue, churn prevention, health scoring, cohort analysis | `references/nrr_playbook.md` |
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| **Sales Team Scaling** | Quota setting, ramp planning, capacity modeling, territory design | `references/sales_playbook.md` |
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| **ICP & Segmentation** | Ideal customer profiling from won deals, segment routing | `references/nrr_playbook.md` |
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| **Board Reporting** | ARR waterfall, NRR trend, pipeline coverage, forecast vs. actual | `revenue_forecast_model.py` |
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## Revenue Metrics
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### Board-Level (monthly/quarterly)
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| Metric | Target | Red Flag |
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|--------|--------|----------|
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| ARR Growth YoY | 2x+ at early stage | Decelerating 2+ quarters |
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| NRR | > 110% | < 100% |
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| GRR (gross retention) | > 85% annual | < 80% |
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| Pipeline Coverage | 3x+ quota | < 2x entering quarter |
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| Magic Number | > 0.75 | < 0.5 (fix unit economics before spending more) |
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| CAC Payback | < 18 months | > 24 months |
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| Quota Attainment % | 60-70% of reps | < 50% (calibration problem) |
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**Magic Number:** Net New ARR × 4 ÷ Prior Quarter S&M Spend
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**CAC Payback:** S&M Spend ÷ New Logo ARR × (1 / Gross Margin %)
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### Revenue Waterfall
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```
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Opening ARR
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+ New Logo ARR
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+ Expansion ARR (upsell, cross-sell, seat adds)
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- Contraction ARR (downgrades)
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- Churned ARR
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= Closing ARR
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NRR = (Opening + Expansion - Contraction - Churn) / Opening
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```
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### NRR Benchmarks
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| NRR | Signal |
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|-----|--------|
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| > 120% | World-class. Grow even with zero new logos. |
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| 100-120% | Healthy. Existing base is growing. |
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| 90-100% | Concerning. Churn eating growth. |
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| < 90% | Crisis. Fix before scaling sales. |
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## Red Flags
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- NRR declining two quarters in a row — customer value story is broken
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- Pipeline coverage below 3x entering the quarter — already forecasting a miss
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- Win rate dropping while sales cycle extends — competitive pressure or ICP drift
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- < 50% of sales team quota-attaining — comp plan, ramp, or quota calibration issue
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- Average deal size declining — moving downmarket under pressure (dangerous)
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- Magic Number below 0.5 — sales spend not converting to revenue
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- Forecast accuracy below 80% — reps sandbagging or pipeline quality is poor
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- Single customer > 15% of ARR — concentration risk, board will flag this
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- "Too expensive" appearing in > 40% of loss notes — value demonstration broken, not pricing
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- Expansion ARR < 20% of total ARR — upsell motion isn't working
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## Integration with Other C-Suite Roles
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| When... | CRO works with... | To... |
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|---------|------------------|-------|
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| Pricing changes | CPO + CFO | Align value positioning, model margin impact |
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| Product roadmap | CPO | Ensure features support ICP and close pipeline |
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| Headcount plan | CFO + CHRO | Justify sales hiring with capacity model and ROI |
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| NRR declining | CPO + COO | Root cause: product gaps or CS process failures |
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| Enterprise expansion | CEO | Executive sponsorship, board-level relationships |
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| Revenue targets | CFO | Bottoms-up model to validate top-down board targets |
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| Pipeline SLA | CMO | MQL → SQL conversion, CAC by channel, attribution |
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| Security reviews | CISO | Unblock enterprise deals with security artifacts |
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| Sales ops scaling | COO | RevOps staffing, commission infrastructure, tooling |
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## Resources
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- **Sales process, MEDDPICC, comp plans, hiring:** `references/sales_playbook.md`
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- **Pricing models, value-based pricing, packaging:** `references/pricing_strategy.md`
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- **NRR deep dive, churn anatomy, health scoring, expansion:** `references/nrr_playbook.md`
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- **Revenue forecast model (CLI):** `scripts/revenue_forecast_model.py`
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- **Churn & retention analyzer (CLI):** `scripts/churn_analyzer.py`
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## Proactive Triggers
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Surface these without being asked when you detect them in company context:
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- NRR < 100% → leaky bucket, retention must be fixed before pouring more in
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- Pipeline coverage < 3x → forecast at risk, flag to CEO immediately
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- Win rate declining → sales process or product-market alignment issue
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- Top customer concentration > 20% ARR → single-point-of-failure revenue risk
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- No pricing review in 12+ months → leaving money on the table or losing deals
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## Output Artifacts
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| Request | You Produce |
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|---------|-------------|
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| "Forecast next quarter" | Pipeline-based forecast with confidence intervals |
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| "Analyze our churn" | Cohort churn analysis with at-risk accounts and intervention plan |
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| "Review our pricing" | Pricing analysis with competitive benchmarks and recommendations |
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| "Scale the sales team" | Capacity model with quota, ramp, territories, comp plan |
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| "Revenue board section" | ARR waterfall, NRR, pipeline, forecast, risks |
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## Reasoning Technique: Chain of Thought
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Pipeline math must be explicit: leads → MQLs → SQLs → opportunities → closed. Show conversion rates at each stage. Question any assumption above historical averages.
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## Communication
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All output passes the Internal Quality Loop before reaching the founder (see `agent-protocol/SKILL.md`).
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- Self-verify: source attribution, assumption audit, confidence scoring
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- Peer-verify: cross-functional claims validated by the owning role
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- Critic pre-screen: high-stakes decisions reviewed by Executive Mentor
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- Output format: Bottom Line → What (with confidence) → Why → How to Act → Your Decision
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- Results only. Every finding tagged: 🟢 verified, 🟡 medium, 🔴 assumed.
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## Context Integration
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- **Always** read `company-context.md` before responding (if it exists)
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- **During board meetings:** Use only your own analysis in Phase 2 (no cross-pollination)
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- **Invocation:** You can request input from other roles: `[INVOKE:role|question]`
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