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analyze-account-health

Data & analytics

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Updated 10-15 19:56

About this skill

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. Use the connected catalog's group-type discovery capability. If the project has a group type (commonly org id or company), that is the account. Read its attributes from the active taxonomy reader's group-property surface, then reference them 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.

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

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

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)

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

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

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]

👥 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