[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MFDZXJ0GS0R0G0112D3XMS":3,"skill-same:01M6MFDZXJ0GS0R0G0112D3XMS":71},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":13,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":56,"install":57,"resources":70},"01M6MFDZXJ0GS0R0G0112D3XMS","chief-customer-officer-advisor",null,"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\u002Fbusiness-growth tactical skills.","JiayiV\u002Fquestion_mixing","JiayiV","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54466889?v=4","01M6JFJYYMRZ3QRGP9339JNQ2K","01M6JFJZ35KB0QVT50NAHPJA7G",0,"MIT","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-gemini-skills-chief-customer-officer-advisor-skill-05c50e9e\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"chief customer officer advisor","advisor","chief","customer","scoring","customer segmentation","sequencing","churn","retention","icp","decomposition","leadership","across","tactical","only","coverage","math","model","team","segmentation","2026-11-10 10:12:30",false,"---\nname: \"chief-customer-officer-advisor\"\ndescription: \"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\u002Fbusiness-growth tactical skills.\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: c-level\n  domain: chief-customer-officer-leadership\n  updated: 2026-05-13\n  python-tools: retention_decomposition_analyzer.py, customer_segmentation_designer.py, cs_coverage_calculator.py\n  frameworks: retention-decomposition, customer-segmentation, cs-coverage-model, cs-team-org\n---\n\n# Chief Customer Officer Advisor\n\nStrategic customer leadership for startup CCOs and founders without one. **Four decisions, no generic CS survey:**\n\n1. **What's our retention architecture — and is gross retention vs NRR honest?** — decomposition into gross retention, contraction, expansion + churn root-cause taxonomy\n2. **How do we segment customers for differential investment?** — tier design + ICP fit scoring + investment-per-segment math\n3. **What's the CS team's coverage model — and when do we go pooled vs named?** — coverage ratio calculator + transition thresholds\n4. **What CS role do we hire next?** — stage-to-role map (CS ≠ Support ≠ AM ≠ Implementation)\n\nThis skill does **not** cover tactical CS implementation. For health-score tooling, CRM workflows, NPS survey infrastructure, or onboarding automation, see `business-growth\u002Fcustomer-success-management\u002F` and adjacent tactical skills.\n\n## Keywords\n\nCCO, 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, NPS, CSAT, customer effort score, segmentation, ICP fit, tier design, low-touch, high-touch, tech-touch, pooled CSM, named CSM, customer success manager, account manager, AM, implementation manager, IM, customer success operations, CS ops, book of business, ratio, ARR-per-CSM, customer marketing, advocacy, expansion playbook, voice of customer, VoC\n\n## Quick Start\n\n```bash\n# Decision A: Decompose retention honestly\npython scripts\u002Fretention_decomposition_analyzer.py                          # embedded B2B SaaS sample\npython scripts\u002Fretention_decomposition_analyzer.py path\u002Fto\u002Fcohorts.json\n\n# Decision B: Design customer segmentation + differential investment\npython scripts\u002Fcustomer_segmentation_designer.py                            # embedded 4-tier sample\npython scripts\u002Fcustomer_segmentation_designer.py path\u002Fto\u002Fcustomers.json\n\n# Decision C: Calculate CS team coverage model\npython scripts\u002Fcs_coverage_calculator.py                                    # embedded 350-customer sample\npython scripts\u002Fcs_coverage_calculator.py path\u002Fto\u002Fbook.json\n```\n\n## Key Questions (ask these first)\n\n- **What's your GROSS retention rate?** (Not NRR — NRR hides churn behind expansion. Ask gross first.)\n- **What's the #1 reason customers leave?** (If you can't name it, you don't understand churn.)\n- **What's the median time-to-value (TTV) by segment?** (Long TTV in low tier = misfit; long TTV in high tier = onboarding broken.)\n- **Which customer would you fire today?** (If \"none\" — your segmentation is broken; some accounts cost more than they earn.)\n- **What's your ARR-per-CSM ratio, and what's the model — pooled or named?** (Stage and ACV determine the right answer.)\n- **Is CS in your comp plan, and how is it different from Sales comp?** (CS comp on retention; misalignment is a leading indicator of failure.)\n\n## Core Responsibilities\n\n### 1. Retention Decomposition\n\n**The trap:** \"Our NRR is 115%, retention is great.\"\n\nThe truth: NRR = Gross Retention − Contraction + Expansion. A 115% NRR with 85% gross retention is a leaky bucket masked by upsells. A 115% NRR with 98% gross retention is a healthy product.\n\n**Mandatory decomposition every quarter:**\n\n| Metric | What it measures | Health threshold (B2B SaaS) |\n|---|---|---|\n| **Gross Retention (GRR)** | $ from existing customers minus churn + contraction | ≥ 90% at growth stage; ≥ 95% at scale |\n| **Logo Retention** | % of customers who renewed | ≥ 85% at growth; ≥ 90% at scale |\n| **Net Revenue Retention (NRR)** | GRR + expansion | ≥ 110% at growth; ≥ 120% at scale |\n| **Contraction** | $ from existing customers reducing seats\u002Fusage | \u003C 5% annually |\n| **Expansion** | $ from existing customers growing | 15-25% annually at healthy |\n\n**Run** `retention_decomposition_analyzer.py` with cohort data for honest decomposition + churn root-cause categorization.\n\nSee `references\u002Fretention_decomposition.md` for the 7-category churn taxonomy + leading indicator playbook.\n\n### 2. Customer Segmentation\n\n**The trap:** \"Every customer is important.\"\n\nThe reality: customers exist on a spectrum of ICP fit × strategic value. Treating them identically wastes CS capacity and ignores expansion opportunity.\n\n**4-tier framework (B2B SaaS baseline):**\n\n| Tier | ARR range | Coverage | Investment per account\u002Fyr |\n|---|---|---|---|\n| **Strategic** | Top 5%, often $100K+ | Named CSM + executive sponsor | $20K-50K |\n| **Enterprise** | Next 15-20%, $20K-100K | Named CSM | $5K-15K |\n| **Mid-market** | Next 30-40%, $5K-20K | Pooled CSM + automation | $1K-3K |\n| **SMB \u002F Long-tail** | Bottom 40-50%, \u003C$5K | Tech-touch + self-serve | $50-500 |\n\n**Run** `customer_segmentation_designer.py` to design segmentation tiers + differential investment + ICP fit scoring.\n\nSee `references\u002Fcustomer_segmentation_strategy.md` for ICP fit framework, tier transition triggers, and the kill list (customers below the investment floor).\n\n### 3. CS Team Coverage Model\n\n**The trap:** \"Hire one CSM per X customers\" with a single ratio across all segments.\n\nThe reality: coverage model depends on segment, ACV, and complexity. Pooled CSM works for low-touch; named CSM is required for strategic accounts.\n\n**Coverage models:**\n\n| Model | Best for | Ratio (ARR-per-CSM) | Trade-offs |\n|---|---|---|---|\n| **Tech-touch (no human)** | SMB, low ACV | $5M-15M+ | Automation cost; cannot save high-stakes deals |\n| **Pooled CSM** | Mid-market | $2M-5M | Lower cost; less account intimacy |\n| **Named CSM** | Enterprise | $500K-2M | Higher cost; deeper relationships |\n| **Named CSM + exec sponsor** | Strategic | $300K-1M | Highest cost; reserved for top accounts |\n\n**Run** `cs_coverage_calculator.py` with book characteristics to calculate required CSM headcount and identify transition thresholds.\n\nSee `references\u002Fcs_coverage_model.md` for ratios, ramp curves, and the \"when to add a manager\" trigger.\n\n### 4. CS Team Org Evolution\n\n**The wrong question:** \"Should we hire a CSM or a Support engineer?\"\n**The right question:** \"What's the next customer outcome we're failing to deliver, and what role unblocks that?\"\n\n**Critical distinctions (founders confuse these):**\n\n| Role | Owns | Does NOT own |\n|---|---|---|\n| Customer Support | Reactive issue resolution (ticket queue) | Renewal, expansion, success outcomes |\n| Customer Success Manager | Proactive value realization + renewal + expansion lead | Day-to-day tickets, implementation |\n| Account Manager | Commercial relationship + expansion close | Day-to-day success, technical depth |\n| Implementation Manager | Onboarding + go-live | Ongoing success after launch |\n| CS Operations | Tooling, data, analytics, playbooks | Direct customer relationships |\n| Customer Marketing | Advocacy, case studies, references | 1:1 customer relationships |\n\nSee `references\u002Fcs_team_org_evolution.md` for stage-to-role map (seed → late-stage) + the AM-vs-CSM split decision.\n\n## Workflows\n\n### Workflow 1: Quarterly Retention Review (4 hours)\n**Goal:** Decompose retention honestly + identify top-3 churn drivers.\n\n```bash\n# 1. Pull cohort data: closed\u002Fwon by quarter for last 8 quarters\npython scripts\u002Fretention_decomposition_analyzer.py cohorts.json\n# 2. Review GRR \u002F NRR \u002F contraction \u002F expansion separately\n# 3. For each cohort showing GRR \u003C 90%: identify churn root cause (7-category taxonomy)\n# 4. Cross-check with cs-cro-advisor: does the expansion math add up?\n# 5. Cross-check with cs-cpo-advisor: are product gaps driving churn?\n# 6. Output: top-3 leakage points + 90-day mitigation plan\n```\n\n### Workflow 2: Customer Segmentation Audit (1 day)\n**Goal:** Re-segment customer base + reset differential investment.\n\n```bash\n# 1. Build customers.json with ARR, tenure, ICP fit signals\npython scripts\u002Fcustomer_segmentation_designer.py customers.json\n# 2. Identify segment migration (mid-market → enterprise upgrades, downsells)\n# 3. Identify kill list (customers below investment floor)\n# 4. Output: new tier assignment + investment-per-tier + kill list for sales review\n```\n\n### Workflow 3: CS Team Sizing (1 week)\n**Goal:** Size the CS team aligned to book composition + coverage model.\n\n```bash\n# 1. Build book.json with current customer base + planned acquisition\npython scripts\u002Fcs_coverage_calculator.py book.json\n# 2. Calculate required CSM headcount by segment\n# 3. Compare to current team; identify gaps\n# 4. Cross-check with cs-chro-advisor on comp + leveling\n# 5. Cross-check with cs-cfo-advisor on the cost\n# 6. Output: 12-month hiring plan + role sequence\n```\n\n### Workflow 4: CS Team Roadmap (1 week)\n**Goal:** Sequence next 18 months of CS hires aligned to customer outcomes.\n\n1. List top 5 customer outcomes the company is failing to deliver\n2. Map each outcome to the role that unblocks it (CSM \u002F AM \u002F IM \u002F Support \u002F CS Ops)\n3. Sequence hires; respect prerequisite order\n4. Cross-check with cs-chro-advisor\n\n## Output Standards\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of: retention | segmentation | coverage | next hire]\n**The Evidence:** [numbers from the tool, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder can make]\n```\n\n## Adjacent Skills\n\n- `c-level-advisor\u002Fskills\u002Fcro-advisor\u002F` — Revenue math, NRR, expansion comp (CCO owns customer experience; CRO owns revenue math; clean split)\n- `c-level-advisor\u002Fskills\u002Fcpo-advisor\u002F` — Product strategy, JTBD (CCO surfaces product gaps; CPO decides roadmap)\n- `c-level-advisor\u002Fskills\u002Fcmo-advisor\u002F` — Customer marketing, advocacy, references\n- `c-level-advisor\u002Fskills\u002Fcfo-advisor\u002F` — CS team cost, retention-impact-on-revenue math\n- `c-level-advisor\u002Fskills\u002Fchro-advisor\u002F` — CS team hiring + leveling\n- `business-growth\u002F` — Tactical CS execution: health scores, CRM workflows, onboarding tooling\n\n## References\n\n- [retention_decomposition.md](references\u002Fretention_decomposition.md) — GRR vs NRR honest math + 7-category churn taxonomy + leading indicator playbook\n- [customer_segmentation_strategy.md](references\u002Fcustomer_segmentation_strategy.md) — 4-tier framework + ICP fit scoring + tier transition triggers + kill list criteria\n- [cs_coverage_model.md](references\u002Fcs_coverage_model.md) — Coverage model decision (tech-touch \u002F pooled \u002F named \u002F named+exec) + ratio benchmarks + manager-trigger\n- [cs_team_org_evolution.md](references\u002Fcs_team_org_evolution.md) — Stage-to-role map + 6-role definition table (CSM ≠ Support ≠ AM ≠ IM ≠ CS Ops ≠ Customer Marketing) + AM-vs-CSM split decision + anti-patterns\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n**Disclaimer:** Retention benchmarks vary significantly by ACV, segment, and industry. This skill provides B2B SaaS-baseline guidance; consumer SaaS, marketplaces, and hardware all have materially different retention math.\n","# Chief Customer Officer Advisor\n\nStrategic customer leadership for startup CCOs and founders without one. **Four decisions, no generic CS survey:**\n\n1. **What's our retention architecture — and is gross retention vs NRR honest?** — decomposition into gross retention, contraction, expansion + churn root-cause taxonomy\n2. **How do we segment customers for differential investment?** — tier design + ICP fit scoring + investment-per-segment math\n3. **What's the CS team's coverage model — and when do we go pooled vs named?** — coverage ratio calculator + transition thresholds\n4. **What CS role do we hire next?** — stage-to-role map (CS ≠ Support ≠ AM ≠ Implementation)\n\nThis skill does **not** cover tactical CS implementation. For health-score tooling, CRM workflows, NPS survey infrastructure, or onboarding automation, see `business-growth\u002Fcustomer-success-management\u002F` and adjacent tactical skills.\n\n## Keywords\n\nCCO, 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, NPS, CSAT, customer effort score, segmentation, ICP fit, tier design, low-touch, high-touch, tech-touch, pooled CSM, named CSM, customer success manager, account manager, AM, implementation manager, IM, customer success operations, CS ops, book of business, ratio, ARR-per-CSM, customer marketing, advocacy, expansion playbook, voice of customer, VoC\n\n## Quick Start\n\n```bash\n# Decision A: Decompose retention honestly\npython scripts\u002Fretention_decomposition_analyzer.py                          # embedded B2B SaaS sample\npython scripts\u002Fretention_decomposition_analyzer.py path\u002Fto\u002Fcohorts.json\n\n# Decision B: Design customer segmentation + differential investment\npython scripts\u002Fcustomer_segmentation_designer.py                            # embedded 4-tier sample\npython scripts\u002Fcustomer_segmentation_designer.py path\u002Fto\u002Fcustomers.json\n\n# Decision C: Calculate CS team coverage model\npython scripts\u002Fcs_coverage_calculator.py                                    # embedded 350-customer sample\npython scripts\u002Fcs_coverage_calculator.py path\u002Fto\u002Fbook.json\n```\n\n## Key Questions (ask these first)\n\n- **What's your GROSS retention rate?** (Not NRR — NRR hides churn behind expansion. Ask gross first.)\n- **What's the #1 reason customers leave?** (If you can't name it, you don't understand churn.)\n- **What's the median time-to-value (TTV) by segment?** (Long TTV in low tier = misfit; long TTV in high tier = onboarding broken.)\n- **Which customer would you fire today?** (If \"none\" — your segmentation is broken; some accounts cost more than they earn.)\n- **What's your ARR-per-CSM ratio, and what's the model — pooled or named?** (Stage and ACV determine the right answer.)\n- **Is CS in your comp plan, and how is it different from Sales comp?** (CS comp on retention; misalignment is a leading indicator of failure.)\n\n## Core Responsibilities\n\n### 1. Retention Decomposition\n\n**The trap:** \"Our NRR is 115%, retention is great.\"\n\nThe truth: NRR = Gross Retention − Contraction + Expansion. A 115% NRR with 85% gross retention is a leaky bucket masked by upsells. A 115% NRR with 98% gross retention is a healthy product.\n\n**Mandatory decomposition every quarter:**\n\n| Metric | What it measures | Health threshold (B2B SaaS) |\n|---|---|---|\n| **Gross Retention (GRR)** | $ from existing customers minus churn + contraction | ≥ 90% at growth stage; ≥ 95% at scale |\n| **Logo Retention** | % of customers who renewed | ≥ 85% at growth; ≥ 90% at scale |\n| **Net Revenue Retention (NRR)** | GRR + expansion | ≥ 110% at growth; ≥ 120% at scale |\n| **Contraction** | $ from existing customers reducing seats\u002Fusage | \u003C 5% annually |\n| **Expansion** | $ from existing customers growing | 15-25% annually at healthy |\n\n**Run** `retention_decomposition_analyzer.py` with cohort data for honest decomposition + churn root-cause categorization.\n\nSee `references\u002Fretention_decomposition.md` for the 7-category churn taxonomy + leading indicator playbook.\n\n### 2. Customer Segmentation\n\n**The trap:** \"Every customer is important.\"\n\nThe reality: customers exist on a spectrum of ICP fit × strategic value. Treating them identically wastes CS capacity and ignores expansion opportunity.\n\n**4-tier framework (B2B SaaS baseline):**\n\n| Tier | ARR range | Coverage | Investment per account\u002Fyr |\n|---|---|---|---|\n| **Strategic** | Top 5%, often $100K+ | Named CSM + executive sponsor | $20K-50K |\n| **Enterprise** | Next 15-20%, $20K-100K | Named CSM | $5K-15K |\n| **Mid-market** | Next 30-40%, $5K-20K | Pooled CSM + automation | $1K-3K |\n| **SMB \u002F Long-tail** | Bottom 40-50%, \u003C$5K | Tech-touch + self-serve | $50-500 |\n\n**Run** `customer_segmentation_designer.py` to design segmentation tiers + differential investment + ICP fit scoring.\n\nSee `references\u002Fcustomer_segmentation_strategy.md` for ICP fit framework, tier transition triggers, and the kill list (customers below the investment floor).\n\n### 3. CS Team Coverage Model\n\n**The trap:** \"Hire one CSM per X customers\" with a single ratio across all segments.\n\nThe reality: coverage model depends on segment, ACV, and complexity. Pooled CSM works for low-touch; named CSM is required for strategic accounts.\n\n**Coverage models:**\n\n| Model | Best for | Ratio (ARR-per-CSM) | Trade-offs |\n|---|---|---|---|\n| **Tech-touch (no human)** | SMB, low ACV | $5M-15M+ | Automation cost; cannot save high-stakes deals |\n| **Pooled CSM** | Mid-market | $2M-5M | Lower cost; less account intimacy |\n| **Named CSM** | Enterprise | $500K-2M | Higher cost; deeper relationships |\n| **Named CSM + exec sponsor** | Strategic | $300K-1M | Highest cost; reserved for top accounts |\n\n**Run** `cs_coverage_calculator.py` with book characteristics to calculate required CSM headcount and identify transition thresholds.\n\nSee `references\u002Fcs_coverage_model.md` for ratios, ramp curves, and the \"when to add a manager\" trigger.\n\n### 4. CS Team Org Evolution\n\n**The wrong question:** \"Should we hire a CSM or a Support engineer?\"\n**The right question:** \"What's the next customer outcome we're failing to deliver, and what role unblocks that?\"\n\n**Critical distinctions (founders confuse these):**\n\n| Role | Owns | Does NOT own |\n|---|---|---|\n| Customer Support | Reactive issue resolution (ticket queue) | Renewal, expansion, success outcomes |\n| Customer Success Manager | Proactive value realization + renewal + expansion lead | Day-to-day tickets, implementation |\n| Account Manager | Commercial relationship + expansion close | Day-to-day success, technical depth |\n| Implementation Manager | Onboarding + go-live | Ongoing success after launch |\n| CS Operations | Tooling, data, analytics, playbooks | Direct customer relationships |\n| Customer Marketing | Advocacy, case studies, references | 1:1 customer relationships |\n\nSee `references\u002Fcs_team_org_evolution.md` for stage-to-role map (seed → late-stage) + the AM-vs-CSM split decision.\n\n## Workflows\n\n### Workflow 1: Quarterly Retention Review (4 hours)\n**Goal:** Decompose retention honestly + identify top-3 churn drivers.\n\n```bash\n# 1. Pull cohort data: closed\u002Fwon by quarter for last 8 quarters\npython scripts\u002Fretention_decomposition_analyzer.py cohorts.json\n# 2. Review GRR \u002F NRR \u002F contraction \u002F expansion separately\n# 3. For each cohort showing GRR \u003C 90%: identify churn root cause (7-category taxonomy)\n# 4. Cross-check with cs-cro-advisor: does the expansion math add up?\n# 5. Cross-check with cs-cpo-advisor: are product gaps driving churn?\n# 6. Output: top-3 leakage points + 90-day mitigation plan\n```\n\n### Workflow 2: Customer Segmentation Audit (1 day)\n**Goal:** Re-segment customer base + reset differential investment.\n\n```bash\n# 1. Build customers.json with ARR, tenure, ICP fit signals\npython scripts\u002Fcustomer_segmentation_designer.py customers.json\n# 2. Identify segment migration (mid-market → enterprise upgrades, downsells)\n# 3. Identify kill list (customers below investment floor)\n# 4. Output: new tier assignment + investment-per-tier + kill list for sales review\n```\n\n### Workflow 3: CS Team Sizing (1 week)\n**Goal:** Size the CS team aligned to book composition + coverage model.\n\n```bash\n# 1. Build book.json with current customer base + planned acquisition\npython scripts\u002Fcs_coverage_calculator.py book.json\n# 2. Calculate required CSM headcount by segment\n# 3. Compare to current team; identify gaps\n# 4. Cross-check with cs-chro-advisor on comp + leveling\n# 5. Cross-check with cs-cfo-advisor on the cost\n# 6. Output: 12-month hiring plan + role sequence\n```\n\n### Workflow 4: CS Team Roadmap (1 week)\n**Goal:** Sequence next 18 months of CS hires aligned to customer outcomes.\n\n1. List top 5 customer outcomes the company is failing to deliver\n2. Map each outcome to the role that unblocks it (CSM \u002F AM \u002F IM \u002F Support \u002F CS Ops)\n3. Sequence hires; respect prerequisite order\n4. Cross-check with cs-chro-advisor\n\n## Output Standards\n\n```\n**Bottom Line:** [one sentence — decision and rationale]\n**The Decision:** [one of: retention | segmentation | coverage | next hire]\n**The Evidence:** [numbers from the tool, not adjectives]\n**How to Act:** [3 concrete next steps]\n**Your Decision:** [the call only the founder can make]\n```\n\n## Adjacent Skills\n\n- `c-level-advisor\u002Fskills\u002Fcro-advisor\u002F` — Revenue math, NRR, expansion comp (CCO owns customer experience; CRO owns revenue math; clean split)\n- `c-level-advisor\u002Fskills\u002Fcpo-advisor\u002F` — Product strategy, JTBD (CCO surfaces product gaps; CPO decides roadmap)\n- `c-level-advisor\u002Fskills\u002Fcmo-advisor\u002F` — Customer marketing, advocacy, references\n- `c-level-advisor\u002Fskills\u002Fcfo-advisor\u002F` — CS team cost, retention-impact-on-revenue math\n- `c-level-advisor\u002Fskills\u002Fchro-advisor\u002F` — CS team hiring + leveling\n- `business-growth\u002F` — Tactical CS execution: health scores, CRM workflows, onboarding tooling\n\n## References\n\n- [retention_decomposition.md](references\u002Fretention_decomposition.md) — GRR vs NRR honest math + 7-category churn taxonomy + leading indicator playbook\n- [customer_segmentation_strategy.md](references\u002Fcustomer_segmentation_strategy.md) — 4-tier framework + ICP fit scoring + tier transition triggers + kill list criteria\n- [cs_coverage_model.md](references\u002Fcs_coverage_model.md) — Coverage model decision (tech-touch \u002F pooled \u002F named \u002F named+exec) + ratio benchmarks + manager-trigger\n- [cs_team_org_evolution.md](references\u002Fcs_team_org_evolution.md) — Stage-to-role map + 6-role definition table (CSM ≠ Support ≠ AM ≠ IM ≠ CS Ops ≠ Customer Marketing) + AM-vs-CSM split decision + anti-patterns\n\n---\n\n**Version:** 1.0.0\n**Status:** Production Ready\n**Disclaimer:** Retention benchmarks vary significantly by ACV, segment, and industry. This skill provides B2B SaaS-baseline guidance; consumer SaaS, marketplaces, and hardware all have materially different retention math.\n",{"name":5,"license":14,"metadata":43,"description":7},{"author":44,"domain":45,"updated":46,"version":47,"category":48,"frameworks":49,"python-tools":50},"Alireza Rezvani","chief-customer-officer-leadership",1778630400000,"1.0.0","c-level","retention-decomposition, customer-segmentation, cs-coverage-model, cs-team-org","retention_decomposition_analyzer.py, customer_segmentation_designer.py, cs_coverage_calculator.py","main","068c2486ccc0f8d200a3db4d21ec137064eda3b4","skills\u002Fpublic-02-claude-skills-main-gemini-skills-chief-customer-officer-advisor-skill-05c50e9e","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing","https:\u002F\u002Fgithub.com\u002FJiayiV","2026-08-25 16:30:52",{"source":8,"skillName":5,"commands":58,"prompt":68,"zipAvailable":69},[59,62,65],{"tool":60,"command":61},"npx","npx skills add JiayiV\u002Fquestion_mixing --skill chief-customer-officer-advisor",{"tool":63,"command":64},"bunx","bunx skills add JiayiV\u002Fquestion_mixing --skill chief-customer-officer-advisor",{"tool":66,"command":67},"pnpm","pnpm dlx skills add JiayiV\u002Fquestion_mixing --skill chief-customer-officer-advisor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-gemini-skills-chief-customer-officer-advisor-skill-05c50e9e\u002FSKILL.md",true,[],[72,98,126,159,186,221],{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":78,"publishedAt":38},"01M6NN9PJS9NHWPDG4QP93BP8F","webmcpify","Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for \"webmcpify\", \"add WebMCP\", or \"expose app actions to AI agents\".","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fwebmcpify\u002FSKILL.md",[],[74,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97],"github","ai agents","web","notes","code","browser","https","actions","manifest","inventory","verify","tool","use","ready","unrelated","app","scope","for","webmcp",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":102,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":38},"01M6NN6KZE5RBN6NGHN2WXV1RE","vardoger-analyze","Use when the user asks to personalize the GitHub Copilot CLI assistant, adapt Copilot to their style, use vardoger, or analyze their Copilot CLI conversation history. Reads the local session directory at `~\u002F.copilot\u002Fsession-state\u002F`, extracts recurring preferences and conventions, and writes a fenced personalization block into `~\u002F.copilot\u002Fcopilot-instructions.md`. Runs entirely on the user's machine via the local `vardoger` CLI (`pipx install vardoger`); no network calls and no uploads. Triggers: 'personalize my copilot', 'analyze my copilot history', 'tailor copilot to me', 'run vardoger', 'update my copilot instructions from history', 'make copilot learn my style'.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fvardoger-analyze\u002FSKILL.md",[],[106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125],"vardoger analyze","analyze","directory","cli","github copilot","copilot","run","update","learn","network","my style","install","conversation","me","local","github copilot cli","pipx","my","at","copilot instructions",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":140,"publishedAt":38},"01M6NN3CZGQP3XAJ04BM1NNYGT","transloadit-media-processing","Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS\u002FMP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftransloadit-media-processing\u002FSKILL.md",[132,137],{"categoryNo":133,"categoryName":134,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":138,"categoryName":139,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000023","Skill与智能体开发",[141,142,143,144,145,146,147,148,149,150,151,152,153,91,154,155,156,157,96,158],"transloadit media processing","Media","media processing","processing","MCP","mcp server","documents","subtitles","pipeline","ocr","Video","Audio","mp4","images","files","transformation","free","hls",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":168,"publishedAt":38},"01M6NN28E92F9GH4WFYQ2H1Y15","terraform-azurerm-set-diff-analyzer","Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fterraform-azurerm-set-diff-analyzer\u002FSKILL.md",[165],{"categoryNo":166,"categoryName":167,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000011","运维与部署",[169,170,171,172,173,174,175,176,177,91,32,178,179,180,181,182,183,184,185,96],"terraform","SET","terraform azurerm set diff analyzer","diff","analyzer","diffs","azure","gateway","firewall","resource changes","provider","resources","load balancer","attributes","front","load","balancer",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":203,"publishedAt":38},"01M6NN1QMH2TDYWTH3XY3N5XPV","technical-job-search","Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV\u002Fresume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-job-search\u002FSKILL.md",[192,195,200],{"categoryNo":193,"categoryName":194,"taxonomyType":135,"taxonomyTypeDesc":136},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":196,"categoryName":197,"taxonomyType":198,"taxonomyTypeDesc":199},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",{"categoryNo":201,"categoryName":202,"taxonomyType":198,"taxonomyTypeDesc":199},"01JBSEEDCAT000000000000038","人力资源",[204,205,206,207,208,209,210,211,107,212,91,213,214,215,216,217,218,219,96,220],"search","Job Search","technical job search","technical","writing","tasks","email","parsing","interview","up","engineer","general","activate","do","task","cv","letter",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":227,"publishedAt":38},"01M6NMZFA67XYS1GS09WT42235","steno-mode","Shorthand-first response compression that cuts ~40% of response tokens while preserving technical precision and exact literals. Use when the user says \"steno mode\", \"shorthand mode\", \"compressed responses\", \"token reduction\", \"brief structured output\", or invokes \u002Fsteno. Supports four compression levels: lite, brief, court, machine. Do not trigger for requests needing polished prose such as onboarding\u002Ftutorial content, stakeholder or customer-facing copy, or teaching-focused explanations.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fsteno-mode\u002FSKILL.md",[],[228,229,21,230,231,207,232,91,217,233,234,235,236,237,238,239,240,241,96,242],"steno mode","mode","brief","first","tutorial","teaching","tokens","as","trigger","structured output","machine","output","court","requests","response compression"]