chief-customer-officer-advisor
Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strategy (differential investment across tiers + ICP fit scoring), CS team coverage model (pooled vs named CSM thresholds + ratio math), and CS team org evolution (CS vs Support vs AM distinctions). Use when designing retention strategy, segmenting customers for differential investment, sizing CS team, or sequencing CS hires. Strategic only — does not duplicate engineering/business-growth tactical skills.
Profile →npx skills add alirezarezvani/claude-skills --skill chief-customer-officer-advisor --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Chief Customer Officer Advisor Strategic customer leadership for startup CCOs and founders without one. **Four decisions, no generic CS survey:** 1. **What's our retention architecture — and is gross retention vs NRR honest?** — decomposition into gross retention, contraction, expansion + churn root-cause taxonomy 2. **How do we segment customers for differential investment?** — tier design + ICP fit scoring + investment-per-segment math 3. **What's the CS team's coverage model — and when do we go pooled vs named?** — coverage ratio calculator + transition thresholds 4. **What CS role do we hire next?** — stage-to-role map (CS ≠ Support ≠ AM ≠ Implementation) This skill does **not** cover tactical CS implementation. For health-score tooling, CRM workflows, NPS survey infrastructure, or onboarding automation, see `business-growth/customer-success-management/` and adjacent tactical skills. ## Keywords CCO, chief customer officer, customer success, retention strategy, gross retention, net retention, NRR, GRR, logo retention, dollar retention, churn, contraction, expansion, downsell, customer lifetime value, CLV, LTV, time-to-value, TTV, time-to-first-value, customer health score, NP
- Keywords
- Quick Start
- Key Questions (ask these first)
- Core Responsibilities
- 1. Retention Decomposition
- 2. Customer Segmentation
- 3. CS Team Coverage Model
- 4. CS Team Org Evolution
- Workflows
- Workflow 1: Quarterly Retention Review (4 hours)
- Workflow 2: Customer Segmentation Audit (1 day)
- Workflow 3: CS Team Sizing (1 week)
- Workflow 4: CS Team Roadmap (1 week)
- Output Standards
Decision A: Decompose retention honestly python scripts/retention_decomposition_analyzer.py # embedded B2B SaaS sample python scripts/retention_decomposition_analyzer.py path/to/cohorts.json Decision B: Design customer segmentation + differential investment python scripts/customer_segmentation_designer.py # embedded 4-tier sample python scripts/customer_segmentation_designer.py path/to/customers.json Decision C: Calculate CS team coverage model python scripts/cs_coverage_calculator.py # embedded 350-customer sample python scripts/cs_coverage_calculator.py path/to/book.json python scripts/retention_decomposition_analyzer.py cohorts.json
What does the chief-customer-officer-advisor skill do?
Chief Customer Officer advisory for startups: retention decomposition (gross retention vs NRR honesty, churn root-cause taxonomy), customer segmentation strategy (differential investment across tiers + ICP fit scoring), CS team coverage model (pooled vs named CSM thresholds + ratio math), and CS team org evolution (CS vs Support vs AM distinctions). Use when designing retention strategy, segmenting customers for differential investment, sizing CS team, or sequencing CS hires. Strategic only — does not duplicate engineering/business-growth tactical skills.
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill chief-customer-officer-advisor --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.