Aj Geddes Useful AI Prompts Kpi Dashboard Design

KPI Dashboard Design

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Effective KPI dashboards make performance visible, enable data-driven decisions, and help teams align around shared goals.

When to Use

• Creating performance measurement systems
• Leadership reporting and visibility
• Operational monitoring
• Project progress tracking
• Team performance management
• Customer health monitoring
• Financial reporting

Quick Start

Minimal working example:

# Select relevant, measurable KPIs

class KPISelection:
    KPI_CRITERIA = {
        'Relevant': 'Directly aligned with business strategy',
        'Measurable': 'Can be quantified and tracked',
        'Actionable': 'Team can influence the metric',
        'Timely': 'Measured frequently (daily/weekly)',
        'Bounded': 'Has clear target/threshold',
        'Simple': 'Easy to understand'
    }

    def identify_business_goals(self):
        """Map goals to KPIs"""
        return {
            'Revenue Growth': [
                'Monthly Recurring Revenue (MRR)',
                'Annual Recurring Revenue (ARR)',
                'Customer Lifetime Value (CLV)',
                'Average Revenue Per User (ARPU)'
            ],
            'Customer Acquisition': [
                'Customer Acquisition Cost (CAC)',
                'Conversion Rate',
                'Traffic to Lead Rate',
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Start with business goals, not data
• Limit dashboards to 5-7 core metrics
• Include both leading and lagging indicators
• Assign clear metric ownership
• Update dashboards regularly
• Make drill-down available
• Use visual hierarchy effectively
• Test with actual users
• Include context and benchmarks
• Document metric definitions

❌ DON'T

• Create dashboards without clear purpose
• Include too many metrics (analysis paralysis)
• Forget about data quality
• Build without stakeholder input
• Use confusing visualizations
• Leave dashboards stale
• Ignore mobile viewing experience
• Skip training on dashboard usage
• Create metrics no one can influence
• Change metrics frequently

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