Why (and When) the Shapley Analyser Is Better Than Traditional Methods in Marketing

In marketing, knowing what matters is more valuable than simply knowing what correlates. That’s where the Shapley Analyser stands out—especially when you're trying to explain customer behaviour, not just predict it.

Most marketers use regression models, correlation analysis, or basic machine learning to make sense of survey data. These tools can find patterns—but when it comes to understanding which factors truly drive outcomes like NPS, satisfaction, or churn, they often fall short.

Here’s why, and how the Shapley Analyser changes the game.

💥 Traditional Methods Can Mislead

Say you’re trying to work out what drives your Net Promoter Score (NPS):

• Is it trust?

• Is it speed of service?

• Is it pricing?

A linear regression might tell you which factor is “statistically significant,” but:

• Results depend heavily on variable order and multicollinearity

• Correlated factors steal credit from each other

• It often tells you what predicts—not what contributes

That’s not good enough when you're making investment decisions or trying to justify priorities to your leadership team.

✅ What Makes the Shapley Analyser Different

The Shapley Analyser uses a fair-value method from game theory called Shapley value decomposition. It:

• Runs all combinations of variables

• Measures how much each factor adds to the explanatory power

• Assigns each variable a “share” of influence based on its true marginal contribution

The result?A driver map you can trust—clear, intuitive, and mathematically fair.

🎯 When to Use the Shapley Analyser in Marketing

The Shapley Analyser is perfect when your goal is to explain what’s happening and why—not just build a black-box model.

✅ Key Use Cases:

• NPS and Satisfaction SurveysUnderstand what’s truly driving scores, and quantify each factor’s influence—e.g., "Trust explains 42% of NPS variation."

• Customer Retention and ChurnIdentify which factors really impact likelihood to stay—service, price perception, onboarding experience?

• Brand Perception and Message TestingRun experiments and see which brand traits or messages drive overall preference.

• CX Investment DecisionsJustify where to spend—e.g., “Improving onboarding would yield 3× the return of faster support.”

• Internal CommunicationUse clear, ranked charts to get executive buy-in and move from data to action quickly.

🚫 When It’s Not the Right Tool

While powerful, the Shapley Analyser isn’t always ideal. Avoid it if:

• You’re working with 50+ variables and no intention of narrowing them

• You only need fast, rough prediction—not insight

• You require live streaming analytics (for now—batch is best)

🧠 The Bottom Line

The Shapley Analyser helps marketers turn data into strategy.It doesn’t just answer what—it answers why. And in a competitive landscape, that clarity can be the edge.

Forget confusing coefficients.Forget explaining R-squared to non-data people.Just show them a ranked, evidence-based driver map—and act.

🔍 Want to try it? The Shapley Analyser is free for your first 3 calculations—no code, no fuss. Upload your survey data and see what’s really driving your results. www.shapleyregression.com