What Actually Drives Satisfaction?

How it works

The analysis scores each driver on two lenses. Importance is how tightly the driver tracks the outcome, measured with a standardized regression coefficient and a plain Pearson correlation; relative importance is each driver's share of the combined standardized effect, scaled to add to 100. Performance is the driver's average rating rescaled to a 0-100 score, using the rating scale's range when one is detected. Crossing importance against performance at their medians places every driver in one of four quadrants (Maintain, Improve / Priority, Monitor, or Reduce effort) and the model's R-squared reports how much of the outcome the mapped drivers explain overall.

Use it when you have a numeric outcome (satisfaction, NPS, spend, a score) and a set of attribute ratings, and you want to know which attributes matter most and where to act first.

Not for a yes/no outcome (use logistic driver analysis), a single driver (use correlation), or when you need proven cause-and-effect (this is correlational).

What data do you need?

One row per survey respondent with an outcome score and attribute ratings:

Minimum 30 rows · Best with 100-20,000 responses with 3-10 driver ratings

What's in the report?

Standard-library analysis: which of the attributes you measure most move an outcome you care about: satisfaction, NPS, retention, or spend? Key Driver Analysis combines statistical IMPORTANCE (how tightly each attribute tracks the outcome, via standardized regression effects and correlations) with current PERFORMANCE (how each attribute is rated today) and places every driver on an action quadrant: Maintain, Improve / Priority, Monitor, or Reduce effort. It tells you not just what matters, but where to invest first. Works on any dataset: map a numeric outcome and up to 10 numeric driver ratings.