customer-success
/customer-success
Projektuje system operacyjny Customer Success: model pokrycia segmentów, scoring zdrowia klienta, playbooki ryzyka, przeglądy wartości i ekspansję powiązaną z NRR.
- Autor
- varunk130 ↗
- Repozytorium
- ai-gtm-skill-library ↗
- Licencja
- MIT
- Ostatnia zmiana repozytorium
- 17.10.2026
Ocena i bezpieczeństwo
- Pliki w paczce
- 1
- Skrypty
- 0
- Komentarze HTML
- 0
- Adresy URL w SKILL.md
- 0
Brak sygnałów. Repozytorium zmienione 17.10.2026, bez odwołań do wycofanych modeli i API.
MIT, otwarta: można używać i zmieniać, z zachowaniem informacji o autorze.
Co robi i kiedy
Projektuje system operacyjny Customer Success: model pokrycia segmentów, scoring zdrowia klienta, playbooki ryzyka, przeglądy wartości i ekspansję powiązaną z NRR.
Oryginalny opis (EN)
Customer Success operating system - segmented coverage model, health scoring, risk playbooks, value reviews, and expansion motions tied to net revenue retention. Use when: customer success strategy, CS operating model, health score, risk playbook, EBR / QBR design, churn prevention, net retention, NRR, GRR, success plan, value realization, customer journey post-sale.
Jak działa
- Core Principle
- The THRIVE Framework
- Coverage Tiering
- Health Score Design
- Risk Playbooks
- Output
- Process
- Tips
- Pairs With
Podgląd SKILL.md
--- name: customer-success description: 'Customer Success operating system - segmented coverage model, health scoring, risk playbooks, value reviews, and expansion motions tied to net revenue retention. Use when: customer success strategy, CS operating model, health score, risk playbook, EBR / QBR design, churn prevention, net retention, NRR, GRR, success plan, value realization, customer journey post-sale.' --- # Customer Success (THRIVE Framework) Design a Customer Success operating system that turns post-sale into a predictable revenue engine. The skill enforces explicit coverage tiering, a health score that actually predicts churn, named risk playbooks, value reviews that earn renewals, and expansion motions that move NRR - instead of a generic "we should do QBRs" plan. ## Core Principle **Customer Success is a *coverage and signal* problem, not a relationship problem.** Most CS teams over-invest in friendly check-ins with healthy accounts and under-invest in early risk signals and value proof. THRIVE forces tiered coverage, leading indicators, and renewal-defensible value evidence. ## The THRIVE Framework | Letter | Stage | The Question | |--------|-------|--------------| | **T** | Tier the Book | Which accounts get high-touch, tech-touch, or pooled coverage - and why? | | **H** | Health Scoring | What 4-6 leading signals predict churn 90+ days out? | | **R** | Risk Playbooks | When a signal trips, what named play runs in what timeframe? | | **I** | Insight Reviews | What evidence of value gets shown at each review cadence? | | **V** | Value Realization | How is realized ROI captured and quantified before renewal? | | **E** | Expansion Motion | Which signals trigger which expansion play, and who owns the handoff? | ## Coverage Tiering | Tier | ARR Band | Coverage | Cadence | Primary Goal | |------|----------|----------|---------|--------------| | **High-touch** | Top 10-20% of ARR | Named CSM, exec sponsor | Monthly check-in + quarterly EBR | NRR > 120% | | **Mid-touch** | Mid 30-60% | Pooled CSM, named for risks | Quarterly value review | GRR > 92% | | **Tech-touch** | Long tail | Digital programs, in-product nudges | Automated lifecycle | Self-serve renewal > 80% | ## Health Score Design A useful health score uses **leading**, not lagging, signals. Lagging scores (NPS, ticket volume, login count) confirm churn after it's already locked in. | Signal Type | Example | Why It Leads | |-------------|---------|--------------| | **Adoption depth** | % of paid seats active weekly on critical workflows | Predicts contract value justification | | **Outcome attainment** | Customer-defined success metric progress | Predicts renewal defensibility | | **Stakeholder coverage** | # of named champions + exec sponsor engaged in last 90 days | Predicts survivability of champion change | | **Support signal velocity** | Critical-severity tickets trend (not count) | Predicts frustration cliff | | **Commercial signal** | Procurement contact, contract questions, late payment | Predicts negotiation posture | ## Risk Playbooks Every health-score drop triggers a **named play** with an owner and a deadline: | Trigger | Play | Owner | SLA | |---------|------|-------|-----| | Champion leaves | "Land the new champion" - re-onboarding kit + exec re-intro within 14 days | CSM + AE | 14 days | | Adoption drop > 20% MoM | Root-cause workshop + adoption sprint | CSM + Solutions | 21 days | | Outcome miss flagged | Outcome reset + escalation to exec sponsor | CSM + VP CS | 30 days | | Stakeholder ghosting > 30 days | Multi-thread re-engagement + alternative-stakeholder hunt | CSM + AE | 21 days | ## Output Save to `outputs/customer-success-[motion]-[YYYY-MM-DD].md` | Artifact | Description | |----------|-------------| | **Coverage Model** | Tier definitions, account assignment rules, CSM ratios | | **Health Score Spec** | 4-6 signals, weights, thresholds, decay rules | | **Risk Play Library** | Named plays with owners, SLAs, success criteria | | **Review Cadence Calendar** | EBR / QBR / value-review template by tier | | **Renewal Defense Pack** | Value evidence pack template (ROI, adoption proof, outcome attainment) | | **Expansion Map** | Signals → plays → handoff rules between CSM and AE | | **CS Operating Metrics** | NRR, GRR, logo retention, time-to-value, expansion ARR | ## Process 1. **Tier the book** with explicit revenue and strategic-fit rationale; cut tiers that don't justify the coverage cost 2. **Define 4-6 health signals** - at least 3 must be leading; document weights and decay 3. **Author named risk plays** for the top 6-8 trigger conditions; every play has an owner + SLA 4. **Design the review cadence** per tier with a fixed agenda; value evidence is non-negotiable 5. **Build the renewal defense pack** template - what gets shown 90 days before renewal 6. **Map expansion signals to plays** and codify the CSM → AE handoff ## Tips 1. **Health scores must be defensible** - every signal needs a "why this leads churn" rationale 2. **Coverage ratios are not industry benchmarks** - they're a function of your motion's complexity 3. **EBR/QBR without value evidence is a relationship tax** - make outcomes the agenda 4. **Don't conflate CS with support** - support resolves issues, CS realizes outcomes 5. **Renewal forecasts come from the health score**, not CSM optimism ## Pairs With - **growth-loop** - Retention and expansion strategy at the system level - **journey-architect** - Post-sale journey gates the health signals attach to - **enablement-forge** - Builds the EBR / value review templates - **revenue-analytics** - NRR / GRR analysis the playbooks are scored against
Wersje
| Repozytorium | Wynik | Licencja |
|---|---|---|
| Ta strona · varunk130/ai-gtm-skill-library | 100 | MIT |
| travisjneuman/.claude | 100 | MIT |
| zime-ai/zime-gtm-skills | 100 | MIT |
| vignesh2027/Claude-Agentic-Skills2.0-version | 90 | MIT |
| fatihkan/badi | 85 | MIT |
Migawka bazy z 28.10.2026 · wersja paczki 9d734b660cba (skrót SHA-256 całej paczki)
Pobrania w ostatnich 30 dniach: 0 · ranking
Zgłoś problem z tym skillem: szkodliwa treść, błędny opis albo prośba autora o usunięcie.
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