Driver analysis: what really moves your outcome

Use relative-importance analysis to find which factors drive NPS, satisfaction or loyalty most — computed on the R engine, directly on your weighted data. No export, no R code.

Driver analysis as a bar chart of relative importance in DataLion

Driver analysis in DataLion is a relative-importance analysis: it decomposes the explained variance of an outcome like NPS or satisfaction across the individual drivers and shows which factors explain the result most. It runs on the R engine, complemented by regressions — directly on your weighted data.

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Which drivers explain your outcome

The score alone does not tell you why it moves. In DataLion, driver analysis is a relative-importance analysis: it decomposes the explained variance of an outcome across the individual influencing factors and shows which drivers contribute most.

The result is a clear ranking: which factor explains the most, which barely any? So you steer resources toward the drivers that actually move the outcome — instead of gut feeling.

  • Variance decomposition across the individual drivers
  • A clear ranking by importance
  • Focus on the factors with real leverage
  • Robust prioritization instead of gut feeling
Ranking of drivers by relative importance in DataLion

Computed on your data — with no export

Relative-importance analysis is one of the 20+ predefined procedures on DataLion's R engine and runs directly on your weighted dataset. To complement it, you run regressions — linear, ordinal and more — to check the direction and strength of individual effects.

No export to R, SPSS or Python, no re-importing: driver analysis and regression run on the same data as your crosstabs and charts.

  • Relative importance as a predefined R procedure
  • Computed on weighted data
  • Complemented by linear and ordinal regressions
  • No data export, no R code

NPS, satisfaction and loyalty drivers

Driver analysis is the heart of modern customer surveys: which experience dimensions drive the Net Promoter Score? Which factors explain satisfaction or repurchase intent?

Drivers can be compared by segment — so you see whether price works differently for new customers than for existing ones. You also analyze the open why-question in a structured way as net codes.

  • Drivers of NPS and satisfaction
  • Repurchase and loyalty drivers
  • Driver comparison by segment
  • Complemented by structured open-ended responses
Driver comparison across segments in a DataLion dashboard

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More analysis features

Common questions about driver analysis

What is driver analysis in DataLion?
A relative-importance analysis: it decomposes the explained variance of an outcome like NPS or satisfaction across the individual drivers and shows, as a ranking, which factors explain the result most.
What method is behind it?
Relative-importance analysis (variance decomposition) on the R engine, one of the 20+ predefined procedures in DataLion, complemented by regressions such as linear or ordinal models.
What do I use a driver analysis for?
To understand which factors drive an outcome — for example which experience dimensions explain the NPS, satisfaction or loyalty most, and where you should steer resources.
Can I compare drivers by segment?
Yes. You run the driver analysis for different segments and compare whether a factor — such as price — works differently for new customers than for existing ones.
Do I need to export data or know R?
No. The analysis runs on the R engine in the background, directly on your weighted data. You pick the procedure with a click, with no R code and no data export.

Find out what drives your outcome

Try DataLion free: relative importance and regressions directly on your data — with no export. Or book a personal demo.