xiaohongshu
xiaohongshu
executive-dashboard
Эксперт executive dashboards. Используй для KPI визуализации, business intelligence и reporting.
Full skill instructions
Expert in designing and building executive dashboards that deliver actionable insights to C-level executives and senior leadership.
const financialKPIs = {
revenue: {
current: 'Monthly Recurring Revenue (MRR)',
trend: 'Revenue Growth Rate (YoY)',
health: 'Revenue per Employee',
forecast: 'Pipeline Value & Conversion Rate'
},
profitability: {
margin: 'Gross Margin %',
efficiency: 'Operating Expense Ratio',
cash: 'Cash Flow & Burn Rate',
roi: 'Return on Investment by Initiative'
},
unit_economics: {
cac: 'Customer Acquisition Cost',
ltv: 'Customer Lifetime Value',
ltv_cac_ratio: 'LTV:CAC Ratio (target: 3:1)',
payback: 'CAC Payback Period'
}
};
const operationalKPIs = {
customers: {
acquisition: 'Customer Acquisition Cost (CAC)',
retention: 'Net Revenue Retention (NRR)',
satisfaction: 'Net Promoter Score (NPS)',
lifetime: 'Customer Lifetime Value (CLV)',
churn: 'Monthly/Annual Churn Rate'
},
performance: {
quality: 'Defect Rate & SLA Performance',
speed: 'Time to Market & Cycle Time',
capacity: 'Utilization Rates & Capacity Planning'
},
growth: {
pipeline: 'Sales Pipeline Coverage',
conversion: 'Stage Conversion Rates',
velocity: 'Deal Velocity'
}
};
<!-- Top-level executive view -->
<div class="executive-dashboard">
<!-- Hero Metrics (top 20% of screen) -->
<section class="hero-metrics">
<div class="primary-kpi">Revenue: $2.3M ↗️ 12%</div>
<div class="status-indicators">
<span class="green">Growth</span>
<span class="yellow">Margins</span>
<span class="red">Churn</span>
</div>
</section>
<!-- Key Trends (middle 60%) -->
<section class="trend-charts">
<div class="chart-grid">
<chart type="line" data="revenue-trend" period="12mo"/>
<chart type="gauge" data="nps-score" target="50"/>
<chart type="funnel" data="sales-pipeline"/>
<chart type="heatmap" data="regional-performance"/>
</div>
</section>
<!-- Action Items (bottom 20%) -->
<section class="action-items">
<alert type="critical">Customer churn up 3% - immediate action required</alert>
<insight>Marketing ROI improved 24% - scale successful campaigns</insight>
</section>
</div>
.executive-dashboard {
display: grid;
grid-template-areas:
"hero hero hero hero"
"chart1 chart1 chart2 chart2"
"chart3 chart4 chart5 chart6"
"alerts alerts insights insights";
grid-template-columns: repeat(4, 1fr);
grid-template-rows: auto 1fr 1fr auto;
gap: 24px;
padding: 24px;
min-height: 100vh;
}
.hero-metrics {
grid-area: hero;
display: flex;
justify-content: space-between;
align-items: center;
background: linear-gradient(135deg, #1a365d 0%, #2d3748 100%);
border-radius: 12px;
padding: 32px;
}
.primary-kpi {
font-size: 48px;
font-weight: 700;
color: white;
}
.status-indicator {
padding: 8px 16px;
border-radius: 20px;
font-weight: 600;
}
.status-indicator.green { background: #48bb78; }
.status-indicator.yellow { background: #ecc94b; color: #1a202c; }
.status-indicator.red { background: #f56565; }
def select_chart_type(data_type, purpose):
chart_mapping = {
('trend', 'time_series'): 'line_chart',
('comparison', 'categories'): 'bar_chart',
('part_to_whole', 'composition'): 'donut_chart',
('performance', 'target'): 'gauge_chart',
('correlation', 'scatter'): 'scatter_plot',
('geographic', 'regional'): 'choropleth_map',
('process', 'conversion'): 'funnel_chart',
('distribution', 'variance'): 'box_plot',
('ranking', 'top_n'): 'horizontal_bar',
('change', 'waterfall'): 'waterfall_chart'
}
return chart_mapping.get((data_type, purpose), 'table')
# Executive dashboard color palette
EXEC_COLORS = {
'success': '#00A86B', # Green - targets met/exceeded
'warning': '#FFB000', # Amber - attention needed
'critical': '#D2222D', # Red - immediate action required
'neutral': '#708090', # Gray - informational
'primary': '#1f4e79', # Navy - brand/emphasis
'secondary': '#4a5568', # Dark gray - secondary elements
'background': '#f7fafc' # Light gray - backgrounds
}
class ExecutiveDashboard {
constructor() {
this.filters = {
timeframe: 'YTD',
region: 'All',
business_unit: 'All'
};
this.alertThresholds = {
revenue_variance: 0.05,
customer_churn: 0.02,
margin_decline: 0.03
};
}
// Auto-refresh critical metrics
setupRealTimeUpdates() {
setInterval(() => {
this.updateMetrics(['revenue', 'active_users', 'system_health']);
this.checkAlertConditions();
}, 300000); // 5-minute intervals
}
// Contextual drill-downs
enableDrillDown(metric, level = 'summary') {
const drillPaths = {
'revenue': ['total', 'by_product', 'by_region', 'by_customer'],
'churn': ['rate', 'by_segment', 'by_reason', 'cohort_analysis'],
'pipeline': ['total', 'by_stage', 'by_rep', 'by_source']
};
return drillPaths[metric] || ['summary'];
}
// Export for board presentations
exportToPDF() {
return {
format: 'landscape',
pages: ['executive_summary', 'financial', 'operational'],
branding: true,
timestamp: new Date().toISOString()
};
}
}
def generate_executive_insights(metrics_data):
insights = []
# Trend analysis
if metrics_data['revenue_growth'] > 0.15:
insights.append({
'type': 'opportunity',
'message': f"Revenue accelerating at {metrics_data['revenue_growth']:.1%} - consider scaling successful initiatives",
'action': 'Review top-performing channels for expansion'
})
# Anomaly detection
if abs(metrics_data['current_vs_forecast']) > 0.1:
insights.append({
'type': 'alert',
'message': 'Significant variance from forecast detected',
'impact': 'May affect quarterly targets',
'next_steps': 'Schedule forecast review meeting'
})
# Churn warning
if metrics_data['churn_rate'] > metrics_data['churn_threshold']:
insights.append({
'type': 'critical',
'message': f"Churn rate at {metrics_data['churn_rate']:.1%} exceeds threshold",
'impact': f"Potential ARR loss: ${metrics_data['at_risk_arr']:,.0f}",
'next_steps': 'Activate retention playbook'
})
return sorted(insights, key=lambda x: {'critical': 0, 'alert': 1, 'opportunity': 2}[x['type']])
// Board presentation export
function exportToBoardDeck() {
const slideTemplates = {
'executive_summary': {
layout: 'hero_metrics_with_trend',
charts: ['revenue_trend', 'key_kpis_table'],
insights: 'auto_generated'
},
'financial_performance': {
layout: 'financial_grid',
charts: ['revenue_waterfall', 'margin_analysis', 'cash_flow'],
commentary: 'variance_explanation'
},
'operational_highlights': {
layout: 'balanced_scorecard',
charts: ['customer_metrics', 'efficiency_trends'],
actions: 'priority_initiatives'
},
'forward_look': {
layout: 'forecast_view',
charts: ['pipeline_coverage', 'growth_projections'],
risks: 'risk_register_summary'
}
};
return generatePresentation(slideTemplates);
}
-- Executive dashboard data mart optimization
CREATE MATERIALIZED VIEW executive_kpis_daily AS
SELECT
date_key,
SUM(revenue) as total_revenue,
COUNT(DISTINCT customer_id) as active_customers,
AVG(satisfaction_score) as avg_nps,
SUM(revenue) / COUNT(DISTINCT customer_id) as revenue_per_customer,
SUM(CASE WHEN is_churned THEN arr ELSE 0 END) as churned_arr,
SUM(CASE WHEN is_new THEN arr ELSE 0 END) as new_arr
FROM fact_daily_metrics
WHERE date_key >= CURRENT_DATE - INTERVAL '2 years'
GROUP BY date_key;
-- Create index for fast filtering
CREATE INDEX idx_exec_kpis_date ON executive_kpis_daily(date_key);
-- Refresh every 4 hours for near real-time executive view
SELECT cron.schedule('refresh-exec-dashboard', '0 */4 * * *',
'REFRESH MATERIALIZED VIEW CONCURRENTLY executive_kpis_daily;');
const cacheConfig = {
// Hero metrics - most viewed, short cache
heroMetrics: {
ttl: 300, // 5 minutes
preload: true
},
// Trend charts - moderate cache
trendCharts: {
ttl: 900, // 15 minutes
preload: false
},
// Historical data - long cache
historicalData: {
ttl: 3600, // 1 hour
preload: false
},
// Real-time metrics - no cache
realTimeMetrics: {
ttl: 0,
streaming: true
}
};
/* Mobile-first executive dashboard */
@media (max-width: 768px) {
.executive-dashboard {
grid-template-areas:
"hero"
"chart1"
"chart2"
"alerts";
grid-template-columns: 1fr;
padding: 16px;
}
.primary-kpi {
font-size: 32px;
}
.chart-container {
min-height: 200px;
}
/* Touch-friendly controls */
.filter-button {
min-height: 44px;
min-width: 44px;
}
}
def test_dashboard_accuracy():
"""Compare dashboard values to source systems."""
dashboard_revenue = get_dashboard_metric('total_revenue')
source_revenue = query_source_system('SELECT SUM(amount) FROM orders')
variance = abs(dashboard_revenue - source_revenue) / source_revenue
assert variance < 0.001, f"Revenue variance {variance:.2%} exceeds threshold"
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