What is the Driver Analysis?

The Driver Analysis helps you uncover which metrics have the strongest influence on your most important KPI. Instead of guessing why your performance changes, this analysis identifies the true levers driving your results — giving you the clarity to focus effort where it matters most.

It’s not about producing a perfect formula — it’s about exposing causal relationships and relative leverage so your decisions are guided by evidence, not assumption. Think of it as turning intuition about “what might matter” into structured insight about what actually moves the needle.

What You Need to Get Started

To run a Driver Analysis, prepare:

• Primary KPI – The outcome metric you want to understand or improve
• Candidate driver metrics – The supporting metrics you believe influence that KPI
• Historical data – Enough consistent data to detect real relationships rather than random noise

What You’ll Get Back

Running this analysis gives you clear, actionable insights into how your system behaves:

• Driver Influence Ranking – A ranked list of which metrics most strongly affect your KPI
• Leverage Scores – Standardised influence values to compare impact across metrics on the same scale
• Directional Relationships – Visibility into whether each driver moves the KPI positively or negatively
• Contribution Breakdown – A percentage view of how much each driver contributes to changes over time
• Temporal Shifts – Understanding of how driver importance evolves across recent periods

How the Analysis Works

Step 1: Select Your KPI and Candidate Drivers

Start by defining your main KPI and the related metrics you suspect influence it. This sets the scope of your analysis.

Step 2: Map Relationships

You connect the drivers to your KPI to make assumptions explicit — clarifying how influence is expected to flow through your system.

Step 3: Quantify Influence

The analysis evaluates how strongly each driver correlates with and contributes to changes in your KPI. It considers both short-term and delayed effects, giving a fuller picture of influence.

Step 4: Standardise for Comparison

All influence values are normalised so you can compare drivers on a consistent scale, regardless of their units or magnitudes.

Step 5: Surface True Drivers

Metrics with strong and reliable relationships emerge as key drivers — while weak or inconsistent ones are deprioritised or flagged for further review.

How to Interpret the Results

Driver Analysis offers directional clarity, not perfect causation. Its goal is to help you reason about where leverage exists — not to deliver mathematical certainty.

Reliability depends on:

• Data quality – Clean, consistent tracking makes relationships meaningful
• Driver relevance – Selecting logical, behavior-linked metrics avoids false signals
• System complexity – Non-linear or multi-factor relationships may require iteration

Keep in mind:

• Correlation indicates potential influence, not guaranteed causation
• Redundant metrics can blur signals due to overlap
• Seasonality or external shocks can temporarily distort relationships

Bottom line: Treat this analysis as an evidence layer for focus — a way to identify the highest-impact levers in your system, not a verdict on absolute truth.

When to Use the Driver Analysis

Best suited for:

• Diagnosing performance shifts — When your KPI moves and you need to understand why
• Prioritising improvements — When deciding which metrics deserve focus and investment
• Validating assumptions — When you want to test or challenge internal beliefs about what drives results
• Improving forecasting and planning — When building forward-looking strategies rooted in real influence

Avoid or delay if:

• You lack enough consistent data for patterns to emerge
• Your metrics are undefined, inconsistent, or too volatile
• You expect definitive answers rather than strategic guidance

Getting the Best Results

Start With Logical Candidates

Select driver metrics that make intuitive sense — those directly tied to behavior or process, not just correlation.

Simplify the Input Set

Remove redundant or overlapping metrics to improve clarity and reduce noise.

Revisit Regularly

Driver importance shifts over time. Re-run your analysis periodically to see how system dynamics evolve.

Use It as a Strategic Lens

High-impact drivers reveal where leverage lives. Focus energy there, de-emphasise noise elsewhere, and use those insights to align teams around the metrics that truly matter.

Summary

The Driver Analysis helps teams see their system clearly — revealing the hidden leverage behind their results. It replaces intuition with structured evidence and transforms scattered metrics into a hierarchy of influence. By understanding which inputs truly drive your outcomes, you can make sharper, higher-leverage decisions — and build strategies grounded in clarity rather than guesswork.