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skill Claude Code

cco-review — Claude Code skill: retention plan review

This skill pressure-tests plans involving customer retention, segmentation, or Customer Success staffing. Use it before a CSM hire or a change to customer tiers.

Install into Claude Code · Plugin marketplace All methods and platforms ↓
/plugin marketplace add alirezarezvani/claude-skills
/plugin install c-level-agents@claude-code-skills
Open on GitHub

What cco-review does

cco-review asks six questions about gross retention, churn reasons, time to value, costly accounts, CSM capacity, and compensation. The review helps reveal when expansion is masking churn, and whether the service model fits each customer segment.

The skill includes Python scripts: retention_decomposition_analyzer.py, customer_segmentation_designer.py, and cs_coverage_calculator.py. They process cohorts.json, customers.json, and book.json; their paths use ${CLAUDE_PLUGIN_ROOT}. Run it before hiring or changing segments when you have the relevant input files.

What it helps with

  • check gross retention against expansion figures
  • identify churn reasons CS can address
  • flag accounts for downgrade or non-renewal
  • estimate CSM capacity before changing team size

Example prompts for the agent

  • Review this CSM hiring plan: “Add two CSMs for enterprise accounts.”
  • Analyze churn drivers in `cohorts.json` and identify those CS can address.
  • Review customer tiers in `customers.json` and suggest accounts for downgrade or non-renewal.

How to install cco-review

1Plugin marketplace

/plugin marketplace add alirezarezvani/claude-skills
/plugin install c-level-agents@claude-code-skills

This installs the whole c-level-agents plugin with all its skills

2Skills CLI (Vercel)

npx skills add alirezarezvani/claude-skills --skill cco-review -a claude-code

3Manually: put the files in place

~/.claude/skills/cco-review/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/cco-review/

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Questions about cco-review

How do I install cco-review in Claude Code?

Add the marketplace with /plugin marketplace add alirezarezvani/claude-skills, then install the plugin with /plugin install c-level-agents@claude-code-skills.

What does the cco-review skill do?

It reviews plans affecting retention, customer segmentation, and Customer Success staffing through six questions about metrics and operating decisions.

Which files does cco-review use for retention analysis?

Retention analysis uses cohorts.json; segmentation review uses customers.json; coverage sizing uses book.json.

Source text

SKILL.md · MIT
---
name: "cco-review"
description: "/c-level-agents:cco-review [plan] — Retention-obsessed Chief Customer Officer interrogation of any plan that touches customer retention, segmentation, CS team sizing, or CS team hiring. Use when gross retention is slipping, before approving CSM headcount, or when deciding which customer segments to keep or fire."
---

# /c-level-agents:cco-review — CCO Forcing Questions

**Command:** `/c-level-agents:cco-review <plan>`

The retention-obsessed CCO pressure-tests any plan that touches customer experience. Six questions before any retention claim, segmentation change, CS team expansion, or major CS hire.

## When to Run

- Before any board narrative that includes a retention number
- Before approving a CS team headcount expansion
- Before re-segmenting the customer base or changing tier definitions
- Before launching a customer marketing or advocacy program
- Before a major CS hire (CSM, AM, Implementation, Customer Marketing)
- When NRR is "great" but churn complaints from CSMs are increasing
- Before deciding whether to add an AM role separate from CSM

## The Six CCO Questions

### 1. What's the GROSS retention rate?
**Not NRR. Gross.** NRR can hide a leaky bucket behind expansion.
- GRR healthy ≥ 90% at growth stage, ≥ 95% at scale
- If GRR < 85% but NRR > 100%, the product is failing for 15%+ of customers; expansion is masking the failure
- Run `retention_decomposition_analyzer.py`

### 2. What's the #1 reason customers leave?
**If you can't name it, you don't understand churn.**
- 7-category taxonomy: product_fit / competitor_loss / no_value_realized / pricing / champion_left / company_event / tactical_failure
- Preventable churn = product_fit + no_value_realized + tactical_failure
- If preventable > 50%, CS has clear leverage; if < 30%, churn is structural (ICP, market, competition)

### 3. What's the median time-to-value (TTV) by segment?
**Long TTV signals different problems by segment.**
- Long TTV in low tier = ICP misfit; downgrade or kill
- Long TTV in high tier = onboarding broken; fix the Implementation Manager handoff
- TTV is a leading indicator of GRR

### 4. Which customer would you fire today?
**If "none" — your segmentation is broken.**
- Some accounts cost more than they earn (support cost > 50% of ARR + low ICP fit)
- Run `customer_segmentation_designer.py` to surface kill list
- The 3 paths for kill candidates: non-renewal / downgrade-to-tech-touch / raise-price-to-cost-recover

### 5. What's the ARR-per-CSM ratio, and is the model pooled or named?
**Wrong model wastes capacity.**
- Strategic: named + exec sponsor, $300K-$1M ARR/CSM
- Enterprise: named, $500K-$2M
- Mid-market: pooled, $2M-$5M
- SMB: tech-touch, $5M+
- Run `cs_coverage_calculator.py` to size the team

### 6. Is CS in your comp plan, and how is it different from Sales comp?
**Misalignment is the leading indicator of CS failure.**
- CS comp: 70/30 base/variable typical
- Variable: 50% gross retention + 30% net retention + 20% activity
- Anti-pattern: comp CSMs on NPS — they game it
- Anti-pattern: comp CSMs same as Sales — they sell instead of serve

## Workflow

```bash
# 1. Retention decomposition (always start here)
python ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-customer-officer-advisor/scripts/retention_decomposition_analyzer.py cohorts.json

# 2. Segmentation audit
python ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-customer-officer-advisor/scripts/customer_segmentation_designer.py customers.json

# 3. Coverage sizing (if making CS team changes)
python ${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-customer-officer-advisor/scripts/cs_coverage_calculator.py book.json
```

## Output Format

```markdown
# CCO Review: <plan>
**Date:** YYYY-MM-DD

## The Decision Being Made
[one sentence — retention | segmentation | coverage | next hire]

## Retention (if applicable)
- GRR: X% (vs vanity NRR of Y%)
- Top churn driver: <category> at X% of churn
- Preventable churn: X% (CS-controllable)
- Leaky-bucket pattern? yes/no

## Segmentation (if applicable)
- Tier distribution: Strategic X / Enterprise X / Mid-market X / SMB X
- Kill list size: N customers (X% of customers, Y% of ARR)
- Upgrade candidates: N

## Coverage (if applicable)
- Current CSMs: N | Required now: M | Required 12mo: P
- Annual cost (12mo): $X
- Manager trigger fired: yes/no

## Org (if applicable)
- Next hire: <CSM | Support | AM | IM | CS Ops | Customer Marketing>
- Why this, not the alternative: <one line>
- Customer outcome unblocked: <specific>

## Verdict
🟢 SHIP | 🟡 SHARPEN | 🔴 BLOCK

## Next Steps
[3 concrete actions]
```

## Routing

- `/c-level-agents:cpo-review` — if churn root cause is product_fit or no_value_realized
- `/c-level-agents:cro-review` — if expansion math or comp alignment is in question
- `/c-level-agents:cfo-review` — for CS cost commitments and retention-impact-on-revenue
- `cs-chro-advisor` agent — for CS hires, comp, ladder
- `/c-level-agents:decide` — log the verdict
- `/c-level-agents:freeze 30` — on multi-year CS comp plan changes

## Related

- Agent: [`cs-cco-advisor`](../../agents/cs-cco-advisor.md)
- Skill: [`chief-customer-officer-advisor`](${CLAUDE_PLUGIN_ROOT}/vendor/c-level-advisor/chief-customer-officer-advisor/SKILL.md)
- Adjacent: `../../../business-growth/` (tactical CS execution)

---

**Version:** 1.0.0

© Alireza Rezvani · MIT · alirezarezvani/claude-skills

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