Why Traditional Regression Fails with Human Behaviour—and How Shapley Regression Fills the Gap

Understanding what truly drives customer behaviour is a foundational challenge in market research, customer experience (CX), and the social sciences. Whether you're exploring Net Promoter Score (NPS), purchase intent, brand trust, or churn, the goal is the same: identify which factors actually matter, and by how much.

But here’s the problem: most traditional regression methods struggle when variables are humanly—rather than statistically—interrelated.

The Problem: Multicollinearity and Misleading Results

Human decisions are rarely made in isolation. For example, someone’s likelihood to recommend a brand (NPS) might depend on a mix of perceived value, service quality, brand image, and emotional connection—all closely related. In traditional linear regression or stepwise regression, this interrelatedness, known as multicollinearity, causes serious distortion.

Standard regression assumes that the predictor variables are cleanly separable. When two or more variables are correlated—as they almost always are in behavioural data—the model “chooses” between them, suppressing the apparent importance of one while inflating or misattributing significance to another.

As a result, valid, meaningful drivers can be discarded, and insight becomes noise.

Enter Shapley Regression: A More Robust, Fair Approach

Shapley regression, built on cooperative game theory and popularised in economics, provides a solution. Instead of assigning arbitrary weights based on correlation structure, it evaluates each variable’s marginal contribution across all possible combinations. In simple terms, it asks:

“How much does this variable really add, on average, across every scenario?”

This makes it especially effective when dealing with interdependent human factors—a common scenario in:

• NPS driver analysis

• Customer satisfaction research

• Purchase decision modelling

• Brand equity studies

• Employee engagement surveys

Real-World Example: Understanding NPS Drivers

Let’s say a company runs a customer feedback survey that includes:

• Quality of service

• Ease of use

• Price fairness

• Brand reputation

• Emotional connection

A traditional regression model may tell you that “ease of use” is the top driver, while “brand reputation” is negligible. But in reality, these factors are intertwined—customers who find the product easy to use are also likely to view the brand positively. Traditional models can't untangle the shared influence.

Shapley regression, on the other hand, distributes the shared variance fairly. It doesn’t punish a variable for being correlated with others—instead, it reflects its true underlying contribution to NPS.

The result? A clearer, more defensible understanding of what’s really driving customer sentiment—and where to focus improvement efforts.

15 Years of Momentum—And Growing

Shapley regression isn’t new—but in the last 15 years, it has gained serious traction across disciplines. Originally used in economics and political science, it’s now widely adopted in market research, CX strategy, and social science research.

Advances in computing power have made what was once computationally intense now widely accessible—especially via platforms like ShapleyRegression.com, which make it practical for business users, not just statisticians.

Major organisations now use Shapley-based methods to:

• Validate customer value propositions

• Prioritise product features

• Fine-tune brand messaging

• Align CX efforts with what truly drives loyalty

Why ShapleyRegression.com?

At ShapleyRegression.com, we make this powerful method accessible, fast, and usable for real-world business decisions. Upload your survey data, and get a defensible, clear explanation of which drivers matter most—without the distortions and guesswork of traditional methods.

Final Word

When customer behaviour is on the line, flawed methods carry a high cost. With closely related human variables, traditional regression just isn’t enough. Shapley regression brings clarity where others create confusion.

Start using it today—and turn complexity into insight