
CCO Review
FreePressure-test your customer retention plans effectively.
Free · Opens the source repo
What CCO Review does
The CCO Review skill is designed for Chief Customer Officers and customer success teams to rigorously evaluate any plan that impacts customer retention, segmentation, or team structure. By utilizing a structured interrogation approach, this skill prompts users to answer critical questions that directly affect customer experience and retention metrics. It ensures that decisions are grounded in data and strategic thinking, rather than assumptions or incomplete information.
When using the CCO Review skill, users can systematically assess their gross retention rate (GRR) and identify the primary reasons for customer churn. The skill emphasizes the importance of understanding preventable churn and provides actionable insights on customer segmentation. It also helps in determining the appropriate customer success manager (CSM) coverage based on the annual recurring revenue (ARR) per CSM, ensuring that resources are allocated efficiently.
The skill operates through a series of commands that guide users through essential analyses, including retention decomposition, segmentation audits, and coverage sizing. Each command is linked to specific Python scripts that automate complex calculations and provide clear outputs, facilitating informed decision-making. This structured approach is particularly beneficial for organizations experiencing fluctuations in retention metrics or considering significant changes to their customer success strategies.
Overall, the CCO Review skill is a valuable tool for any organization focused on enhancing customer retention and optimizing their customer success operations. It is particularly useful in scenarios where retention is slipping or when evaluating the effectiveness of customer success teams and strategies.
When to use it
Use this skill before making any major decisions related to customer retention, team expansion, or segmentation changes.
When not to use it
This skill is not suitable for situations unrelated to customer retention or where qualitative insights are more appropriate than quantitative analysis.
What you can build with it
Evaluating a New Customer Success Strategy
Before implementing a new strategy aimed at improving retention, use this skill to assess the potential impact on GRR and churn.
Deciding on Customer Segmentation Changes
When considering changes to your customer segments, this skill helps identify which segments are underperforming and should be targeted.
Preparing for a Board Presentation on Retention
Use this skill to gather and analyze data on retention metrics, ensuring you present a well-supported narrative to stakeholders.
How to install CCO Review
View source1. Install with the skills CLI
npx skills add alirezarezvani/claude-skills/cco-review --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by alirezarezvani/cs:cco-review — CCO Forcing Questions
Command: /cs: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.pyto 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.pyto 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
# 1. Retention decomposition (always start here)
python ../../../skills/chief-customer-officer-advisor/scripts/retention_decomposition_analyzer.py cohorts.json
# 2. Segmentation audit
python ../../../skills/chief-customer-officer-advisor/scripts/customer_segmentation_designer.py customers.json
# 3. Coverage sizing (if making CS team changes)
python ../../../skills/chief-customer-officer-advisor/scripts/cs_coverage_calculator.py book.json
Output Format
# 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
/cs:cpo-review— if churn root cause is product_fit or no_value_realized/cs:cro-review— if expansion math or comp alignment is in question/cs:cfo-review— for CS cost commitments and retention-impact-on-revenuecs-chro-advisoragent — for CS hires, comp, ladder/cs:decide— log the verdict/cs:freeze 30— on multi-year CS comp plan changes
Related
- Agent:
cs-cco-advisor - Skill:
chief-customer-officer-advisor - Adjacent:
../../../../business-growth/(tactical CS execution)
Version: 1.0.0
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