Purpose
Turn raw product usage data and behavioral signals into a clear account verdict — health score, the specific risks worth acting on, the expansion opportunities hiding in the data, and the adoption gaps blocking value — so a CSM or Implementation Manager walks into every account conversation knowing exactly what to say and do next.
When to Use
Invoke this skill when the user asks to:
- Assess the health of a specific customer account before a QBR or renewal
- Explain why an account's usage dropped or a specific metric declined
- Find expansion or upsell opportunities inside an existing account's usage
- Diagnose why a customer isn't adopting a feature or hitting activation milestones
- Triage which accounts in a book of business need attention first
- Prepare a renewal risk assessment or save play for an at-risk account
Inputs Required
Before starting, collect:
- Account name and profile: Company name, segment (SMB/Mid-Market/Enterprise), contract value (ARR), and tenure as a customer
- Usage metrics: Login frequency, active users vs. licensed seats, feature adoption breakdown, key action volumes — with a trend window (e.g., last 30/60/90 days vs. prior period)
- Onboarding/activation status: Where the account is against its expected activation milestones or success plan
- Support & engagement signals: Ticket volume and sentiment, NPS/CSAT, exec sponsor engagement, meeting attendance
- Contract context: Renewal date, expansion terms available, any known commercial pressure
- Original success criteria: What the customer said "success" looks like (the use case they bought for)
If trend windows or success criteria are missing, analyze on absolute values and flag the assumption — but note that health verdicts are far weaker without a comparison period.
Instructions
Step 1: Establish the Health Baseline
Compute a directional health verdict from the signals provided. Weight by predictive strength:
- Seat utilization (active users ÷ licensed seats): Below 40% is a strong churn predictor; 40–70% is a watch zone; above 70% is healthy
- Login frequency trend: Declining WoW for 3+ weeks is a leading indicator that precedes ticket silence and non-renewal
- Depth of adoption: Count how many "core value" features are in regular use vs. purchased. Single-feature dependence = fragile account
- Sponsor engagement: An unengaged or departed exec sponsor is the single most common cause of surprise churn
Assign a verdict: Healthy / Watch / At-Risk / Critical — with one sentence stating the dominant driver.
Step 2: Diagnose Churn Risk Signals (Ranked)
Identify and rank the specific risk signals present. For each, state the signal, the evidence in the data, and the severity. Prioritize by predictive weight:
- Declining usage — active users or key actions trending down over the window
- Shallow adoption — customer bought for a use case they aren't executing (map usage back to the original success criteria)
- Single point of failure — usage concentrated in one user or one team; if they leave, the account leaves
- Support friction — rising ticket volume with negative sentiment, or repeated unresolved issues
- Silence — dropping engagement AND no support activity (disengagement, not satisfaction)
- Sponsor risk — champion departed, went quiet, or never attended a review
Rank risks by likelihood-to-cause-churn, not by how obvious they are.
Step 3: Surface Expansion Opportunities
Read the same data for upside. Look for:
- Seat expansion — utilization above 80% or active users approaching the license cap = organic seat demand
- Feature/tier upsell — heavy use of a capped feature, or usage patterns that map to a higher tier
- Cross-team adoption — one team using the product successfully while adjacent teams in the same account have zero activity
- Power-user signal — a cohort using advanced features intensively indicates readiness for premium modules Only flag opportunities with an observable usage trigger — no speculative upsells.
Step 4: Identify Adoption Gaps
Pinpoint where value is being left on the table:
- List purchased-but-unused features tied to the customer's stated use case
- Flag activation milestones not yet reached and how long they've been stalled
- Distinguish capability gaps (they don't know the feature exists / how to use it → enablement fix) from fit gaps (the feature doesn't match their workflow → product/expectation issue)
Step 5: Build the Prioritized Next-Action Plan
Produce 5–7 actions ranked by impact-to-effort. Each action must include:
- The action — a specific, doable step (not "engage the customer")
- Why now — the signal that triggers it
- Owner — CSM / Implementation / Sales / Support
- Timeframe — This week / This month / This quarter
Lead with the single highest-leverage move. If the account is At-Risk or Critical, the top action must be a concrete save play with a named next touchpoint.
Output Format
- Structure: Health verdict banner first, then five diagnostic sections, closing with the ranked action plan
- Length: 700–1,000 words
- Format: Structured document — verdict as a labeled callout, risks/opportunities/gaps as ranked bullet lists, action plan as a numbered priority list or table
- Sections: Health Verdict → Churn Risk Signals (ranked) → Expansion Opportunities → Adoption Gaps → Prioritized Next-Action Plan
- Tone: Customer success director — decisive, evidence-anchored, action-first. Every claim ties to a signal in the data; no hedging on the verdict.
Quality Checklist
Before delivering output, verify:
- [ ] A single health verdict (Healthy/Watch/At-Risk/Critical) is stated with one dominant driver
- [ ] Every churn risk cites the specific metric or signal that supports it — no generic "usage seems low"
- [ ] Risks are ranked by likelihood-to-cause-churn, not listed arbitrarily
- [ ] Every expansion opportunity has an observable usage trigger in the data
- [ ] Adoption gaps are labeled as capability gaps vs. fit gaps
- [ ] Each of the 5–7 next actions has an owner and a timeframe, and the top action is the highest-leverage move
Notes
- Silence is the most misread signal — no tickets and no logins is a churn warning, not a satisfied customer. Treat declining engagement with zero support activity as high severity.
- Map usage back to the original purchase reason wherever possible. An account can look "active" while failing at the exact use case it bought for — that is the account that surprises you at renewal.
- For book-of-business triage, run this per account and sort the verdicts: Critical and At-Risk accounts with near-term renewals get attention first, regardless of ARR.
- Seat utilization and sponsor engagement together predict more renewal outcomes than any single usage metric — check both before finalizing the verdict.
How to use this skill
This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.
Installing in Claude Code:
~/.claude/skills/<folder-name>/SKILL.md— auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)<your-project>/.claude/skills/<folder-name>/SKILL.md— auto-loads only when working in that project (recommended for project-specific skills)
Restart Claude Code or open a new session to register.
Using in Claude.ai, ChatGPT, Gemini, or other AI tools:
- Drag this file into a new chat, or copy and paste its contents
- Send your task or request as your next message — the AI will apply the skill where it fits
For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills