analyze-account-health

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_amp_entities to find existing dashboards, charts, or notebooks for this account. If found, ask user if they want fresh analysis or to review existing.

Resolve how accounts are modelled — do this before any query. Every step below breaks down "by account", and there are three different ways a project represents one. Check in this order and reuse the answer throughout:

1. get_group_types — if the project has a group type (commonly org id or company), that is the account. Account attributes are group properties: get_properties({propertyType: 'group', groupType: '<type>'}), referenced with scope: 'group' plus group_type. To count accounts rather than users, set count_unique_by to the group type.
2. If there is no group type, the account is usually a user property (company, org_name) — scope: 'user'.
3. Failing both, an event property carrying the org (org id, org url).

If a group property comes back as Invalid group property … for group type …, the query engine's registry is missing it — that is a platform gap, not a naming mistake. Do not retry spelling variants. Fall back to the user- or event-level equivalent from (2)/(3), and tell the user which representation you used, since the numbers are not interchangeable.

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Step 1: Quick Health Triage

Use Amplitude:query_amplitude_data to run these queries in parallel:

Usage Trend:

• Event: _active, Metric: uniques, Group by: the account property resolved in Step 0 (with its scope)
• 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_amplitude_data 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_amplitude_data 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:use_amplitude_ai_feedback with facet: "sources" to see what's available.

Get feedback insights: Use Amplitude:use_amplitude_ai_feedback with facet: "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:use_amplitude_ai_feedback with facet: "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	↑↓→	🟢🟡🔴

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🚨 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)

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💡 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]"

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📊 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]

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📎 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