What is a Key Drivers Analysis and what are its benefits?

How it works

In a typical quantitative research study, consumers evaluate a product across multiple attributes (e.g., ease of use, quality, effectiveness) alongside an overall outcome metric (e.g., likelihood to purchase or overall liking). Key Drivers Analysis then quantifies the relationship between each attribute and the outcome, highlighting which attributes are the strongest (and weakest) drivers.

Example

For a men’s razor, a Key Drivers Analysis might reveal that the following attributes most strongly influence purchase intent:

Ease of use
Precision
Glide over skin
Efficiency of the razor

This insight helps teams prioritize improvements and messaging around the features that matter most to customers!

Sample size requirements

Key Drivers Analysis relies on regression-based statistical relationships, and therefore requires a sufficient quantitative sample size to produce reliable results. As a general guideline:

A minimum of 100 total respondents (n=100) is recommended to assess overall drivers in Total
At least 50 respondents per subgroup (n=50) is recommended to get a directional read for specific audiences (e.g., current brand users)

Smaller sample sizes may limit the reliability or interpretability of the results.

When to use Key Drivers Analysis

Key Drivers Analysis is best suited for situations where you want to:

Understand what drives an ideal customer experience
Refine marketing messages based on what resonates most
Focus product improvements or resources on high-impact attributes

How it differs from Sensory Penalty Analysis

While Key Drivers Analysis focuses on what drives overall outcomes, Penalty Analysis is used to understand how specific product attributes influence the overall sensory experience.

For example:

Key Drivers Analysis might show that “glide over skin” drives overall liking for a razor
Penalty Analysis might show that a snack is penalized when it is “not salty enough” or “not crispy enough”

In short:

Key Drivers Analysis = What most influences overall success (e.g., purchase intent, satisfaction)
Penalty Analysis = How specific attribute levels impact the sensory experience

Inputs: What you need to run a Key Drivers Analysis

To run a drivers analysis, you’ll need two types of questions in your survey:

An outcome (dependent variable)
This is the main metric you want to understand or improve. It’s often a question like:

Purchase intent
Overall satisfaction
Likelihood to recommend

Example:
For the men’s razor study noted above, the outcome might be “How likely are you to purchase this razor?”

A set of potential drivers (independent variables)
These are the attributes or features you believe might influence that outcome. Each should be measured with its own question or scale.

Example attributes asked on a 5-pt scale How well does the razor you tested perform on the following attributes?:

Ease of use
Precision
Glide over skin
Efficiency of the razor

Together, these inputs allow the model to evaluate how each attribute relates to the outcome.

Outputs: What you get from a Key Drivers Analysis

A drivers analysis helps you understand what matters most by quantifying the relationship between each attribute and your outcome.

You can expect:

Driver strength (importance scores)
Each attribute is assigned a score showing how strongly it impacts the outcome. This highlights in rank order:

The strongest drivers (biggest influence)
The weaker drivers (less impact)

With this insight, you can clearly see:

Where to focus to improve your outcome (e.g., purchase intent)
Which attributes matter less and may be lower priority

Example insight:
You might find that glide over skin and precision are the strongest drivers of purchase intent, while efficiency has a smaller impact—helping you focus product improvements and messaging where they’ll have the greatest effect.

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Reach out to your Customer Enablement partner or Sales Rep about running a Key Drivers Analysis on Highlight!