AgentSkillsCN

analyze-account-health

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SKILL.md
--- frontmatter
name: analyze-account-health
description: Summarizes B2B account health by analyzing usage patterns, engagement trends, risk signals, and expansion opportunities. Use for customer success reviews, renewal preparation, QBRs, or account prioritization.

Analyze Account Health

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.

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

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

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)

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

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]

Key Metrics

MetricCurrentTrendStatus
MAUX↑↓→ Y%🟢🟡🔴
DAU/MAUX%↑↓→ Y%🟢🟡🔴
License UtilizationX%↑↓→🟢🟡🔴
Features AdoptedX/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]

👥 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]

🎯 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]

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