Key Metric Drivers Report

Overview

The Key Metric Drivers report identifies which metrics have the strongest influence on your primary business metric. By analyzing statistical relationships between metrics, this report helps you understand which factors contribute most significantly to your key performance indicators (KPIs) and guides data-driven decision-making.

When to Use This Report

Use the Key Metric Drivers report when you need to:

Understand What’s Driving Your Results

• Problem: Your KPI is moving, but you don’t know why
• Solution: This report identifies which metrics are actually causing the changes, helping you focus on what matters most

Prioritize Your Efforts

• Problem: You have limited resources and multiple metrics to improve
• Solution: See which metrics have the biggest impact so you can focus your team’s efforts where they’ll make the most difference

Validate Your Assumptions

• Problem: You believe certain factors drive your business, but you’re not sure
• Solution: Statistical analysis confirms or challenges your hypotheses about what really influences your KPIs

Build Better Forecasts

• Problem: You need to predict future performance based on current trends
• Solution: Understanding driver relationships helps you create more accurate forecasts by modeling how changes in one metric affect another

Identify Hidden Relationships

• Problem: Important metric relationships might not be obvious from looking at dashboards
• Solution: Discover non-obvious connections between metrics that could unlock new growth opportunities

Diagnose Performance Issues

• Problem: A key metric is underperforming and you need to find the root cause
• Solution: Trace the problem back to the specific driver metrics that are pulling your KPI down

What It Measures

1. Overall Model Effectiveness

   • The R-Squared value shows what percentage of changes in your main metric can be explained by the dependent metrics you’ve selected.
   • A higher R-Squared value (closer to 100%) indicates that the model effectively captures the key relationships between your metrics.

2. Metric Contributions & Impact

   • The report shows the percentage contribution of each dependent metric, clearly indicating their relative impact on your main metric.
   • Both raw and standardized coefficients are presented, allowing you to compare the influence of different metrics regardless of their scale.

3. Correlations & Significance

   • Correlation coefficients measure the strength and direction of relationships between your metrics.
   • Statistical significance indicators help you determine which relationships are meaningful for decision-making versus those that might be due to chance.

4. Time Series Contributions

   • The report analyzes how each metric’s impact changes over time, helping you identify trends, seasonal patterns, or shifts in metric relationships.
   • This temporal view helps you understand if certain metrics are becoming more or less influential.

How to Create This Report

1. Add metrics to your board

   • Add the main metric you want to analyze
   • Add the potential driver metrics you want to test

2. Connect your metrics

   • Draw connections from each potential driver metric to your main metric

3. Generate the report

   • Select your main metric on the board
   • Click on the “Report” button in the popup menu
   • Choose “Key Metric Drivers” from the report options

Statistical Considerations

Multicollinearity

• The report uses Variance Inflation Factors (VIF) to detect when metrics are too closely related to each other.
• High VIF values indicate that certain metrics may be redundant, potentially distorting your understanding of what’s truly driving your main metric.

Non-Significant Variables

• The report identifies metrics with high correlation but non-significant statistical impact, which may indicate confounding effects.
• Metrics with high p-values (typically above 0.05) might not be reliable drivers and should be evaluated further.