analyze-account-health

Analyze Account Health

On Activation

Before proceeding, append a one-line entry to .wire/execution_log.md:

| YYYY-MM-DD HH:MM | skill | analyze-account-health | activated | B2B account-health analysis triggered this skill |

If .wire/execution_log.md does not exist, create it with the standard header first (see specs/utils/execution_log.md). If no .wire/ directory exists in the current repo, skip this step.

Deep-dive into a B2B account's product usage to prepare for QBRs, assess renewal risk, identify expansion opportunities, or prioritize CS outreach.

Instructions

Step 0: Identify Account & Discover Context

Get the account identifier:

• Company name, org ID, account ID, or group property value
• Ask user if not provided

Search for existing work: Use Amplitude:search to find existing dashboards, charts, or notebooks for this account. If found, ask user if they want fresh analysis or to review existing.

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Step 1: Quick Health Triage

Use Amplitude:query_dataset to run these queries in parallel:

Usage Trend:

• Event: _active, Metric: uniques, Group by: account property
• Time: Last 60 days, daily interval
• Shows: Activity increasing or decreasing?

Engagement Quality:

• Calculate DAU and MAU for account
• Get DAU/MAU ratio (stickiness)
• Shows: How engaged are active users?

User Momentum:

• Active user count week-over-week
• Shows: Team growing or shrinking?

Classify Health:

• Healthy: Growing MAU, DAU/MAU >40%, positive WoW
• At-Risk: Flat/declining MAU, DAU/MAU 20-40%, negative WoW
• Critical: Steep decline, DAU/MAU <20%, sustained negative WoW

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Step 2: User-Level Analysis

Use Amplitude:query_dataset with user-level groupBy:

Power Users:

• Top 3-5 users by event volume (champions to leverage)

Churned Users:

• Users active in previous period but not current (retention risks)

License Utilization:

• Active users in last 30 days vs total seats

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Step 3: Feature Usage Analysis

Use Amplitude:query_dataset grouped by events/features:

Feature Breadth:

• Which core features are being used (ask user for 5-10 key features)
• Adoption rate per feature

Feature Trends:

• Usage over last 90 days per feature
• Identify growing vs declining features

Focus based on health:

• If At-Risk/Critical: Find abandoned features (used 60-90 days ago, not in last 30)
• If Healthy: Find expansion opportunities (premium features not yet tried)

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Step 4: Account Feedback Analysis

Get feedback sources: Use Amplitude:get_feedback_sources to see what's available.

Get feedback insights: Use Amplitude:get_feedback_insights filtered by:

• ampId for each user in the account
• dateStart/dateEnd: Last 90 days
• types: bug, painPoint, complaint, request, lovedFeature

Get specific mentions: For top 3-5 insights, use Amplitude:get_feedback_mentions to get quotes.

Correlate with behavior:

• Complaint about Feature X? Query their usage of Feature X
• Request for Feature Y? Check if they hit limits Y would solve
• Praise for Feature Z? Validate they're heavy users of Z

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Step 5: Present Account Health Report

Structure output as follows:

Account Health Report: [Account Name]

Executive Summary

[2-3 sentences: Health score, key trend, primary recommendation]

Health Score: [🟢 Healthy | 🟡 At-Risk | 🔴 Critical]

[One sentence rationale with key metric]

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Key Metrics

Metric	Current	Trend	Status
MAU	X	↑↓→ Y%	🟢🟡🔴
DAU/MAU	X%	↑↓→ Y%	🟢🟡🔴
License Utilization	X%	↑↓→	🟢🟡🔴
Features Adopted	X/Y	↑↓→	🟢🟡🔴

---

🚨 Risk Factors (if any)

1. [Issue] - [Impact]
   • Usage data: [metric/trend]
   • Customer feedback: [theme with X mentions] - [representative quote]

✅ Positive Signals

1. [What's working] - [Evidence from usage + feedback]

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👥 User Intelligence

Champions (Leverage)

• [User ID/Name]: [Activity summary] - Action: [Specific CS recommendation]

At Risk (Engage)

• [User ID/Name]: [Last active date / declining pattern] - Action: [Check-in recommendation]

Inactive (>30 days)

• [Count] users ([X]% of licenses)

---

💡 Top Pain Points & Requests

Pain Points

1. [Theme] (X mentions)
   • [Concise description]
   • Evidence: [Behavioral data] + "[Quote]" - [Source, Date]
   • Action: [What to do]

Feature Requests

1. [Theme] (X mentions)
   • [What they want]
   • Evidence: "[Quote]" - [Source, Date]
   • Roadmap status: [On roadmap/Not planned/Considering]

What They Love ❤️

1. [Feature]: "[Quote]"

---

📊 Feature Adoption

High Usage: [Feature] - [X users] (↑Y%) Declining: [Feature] - [X users] (↓Y%) - Investigate Untapped (Upsell): [Premium feature] - Could solve [pain point]

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🎯 Recommendations

🔥 This Week

1. [Specific action with user/contact name]

📅 This Month

1. [Strategic action with context]

💰 Expansion Opportunities

1. [Upsell signal with evidence]

---

📎 Details

• Analysis Date: [Date]
• Timeframe: [Last X days]
• Confidence: [High/Medium/Low based on data volume]

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Best Practices

• Always name users - CS needs who to contact, not aggregates
• Connect feedback to behavior - Validate complaints with usage data
• Be specific in recommendations - "Call Sarah about Feature X" not "improve engagement"
• Show trends, not snapshots - Direction matters more than point-in-time
• Flag data gaps - Note low volume, missing properties, or incomplete data
• Prioritize by impact - Focus on issues affecting multiple users or champions

Common Patterns

Churn Risks:

• Champion churned + declining overall usage
• Multiple complaints about same issue + behavioral evidence of friction
• License utilization declining + negative feedback

Expansion Signals:

• Hitting plan limits (users, API, storage)
• Requests for premium features + high engagement
• New users being added + positive feedback