Build Customer Success Metrics Dashboards

This skill is expert in designing, building, and optimizing Customer Success metrics dashboards, covering the critical KPIs that affect retention, expansion, and satisfaction and how to present them in actionable, visually compelling dashboards. Its health score components span product usage metrics (feature adoption, login frequency, session duration, API calls), engagement metrics (support ticket sentiment, training completion, community participation), business metrics (license utilization, user growth, contract value realization), and relationship metrics (executive engagement, QBR completion, NPS scores). Its key performance indicators cover retention (Gross/Net Revenue Retention, Logo Retention, Churn Rate), expansion (upsell rate, cross-sell conversion, expansion ARR), satisfaction (NPS, CSAT, Customer Effort Score), and operational metrics (Time to Value, support response time, onboarding completion rate). It covers dashboard architecture including an executive summary SQL query aggregating active customers, ARR, average health score, and net expansion rate, a weighted health-scoring algorithm combining product usage, engagement, support sentiment, and payment history, React KPI card components with target-vs-actual coloring, SQL cohort retention analysis, a RandomForestClassifier-based churn prediction model generating prioritized intervention alerts, YAML-based real-time alert configuration, and a multi-source ETL pipeline joining CRM, product, support, and billing data.

Use this skill when building a CS dashboard that needs to go beyond raw metric display into automated health scoring, churn prediction, and workflow-triggering alerts - every metric here is meant to map to a specific action (a low health score auto-assigns a CSM task and intervention playbook, a usage decline triggers onboarding resource recommendations, a high expansion score creates a sales opportunity, a support escalation notifies the CSM with ticket context). It covers performance and scale guidance too: caching aggregated metrics at 15-minute intervals, real-time data only for critical alerts, daily pre-computed cohort analysis, materialized views for complex joins, lazy loading for customer detail views, and date/customer_id indexing. It is not meant for a purely visual, non-actionable reporting dashboard - the skill's own closing principle is that every metric must connect to a specific, actionable workflow.

### Python health scoring algorithm
def calculate_health_score(customer_data):
    weights = {
        'product_usage': 0.35,
        'engagement': 0.25,
        'support_sentiment': 0.20,
        'payment_history': 0.20
    }
    
    scores = {
        'product_usage': min(customer_data['daily_active_users'] / customer_data['licensed_users'], 1.0),
        'engagement': customer_data['training_completion_rate'],
        'support_sentiment': customer_data['avg_ticket_sentiment'],
        'payment_history': 1.0 if customer_data['days_overdue'] == 0 else max(0, 1 - customer_data['days_overdue'] / 90)
    }
    
    health_score = sum(score * weights[metric] for metric, score in scores.items()) * 100
    
    if health_score >= 80:
        return {'score': health_score, 'status': 'Healthy', 'color': '#22c55e'}
    elif health_score >= 60:
        return {'score': health_score, 'status': 'At Risk', 'color': '#f59e0b'}
    else:
        return {'score': health_score, 'status': 'Critical', 'color': '#ef4444'}

Given multi-source customer data (CRM, product usage, support, billing), the skill produces a weighted health score with status classification like the one above, an executive KPI summary query, cohort retention SQL analysis, churn-risk alerts prioritized High/Medium with a recommended action, YAML alert rules that trigger tasks, Slack notifications, or sales opportunities on threshold breach, and an ETL pipeline that joins and derives health score, churn risk, and expansion score from raw source data.

Customer Success operations and analytics teams building a dashboard that needs to drive proactive account management, not just report metrics after the fact. It suits teams with multiple data sources (CRM, product telemetry, support tickets, billing) that want health scoring, churn prediction, and cohort analysis wired directly into CSM workflows, with performance patterns (caching, materialized views, lazy loading) sized for large customer bases.