Customers who rate delivery reliability highly also give strong overall satisfaction scores. A driver analysis may rank reliability near the top of the list, but the word “driver” can overstate the result. The relationship is evidence worth investigating, not yet proof of how much satisfaction would improve if delivery reliability changed.

Driver analysis examines which measured factors are associated with an outcome of interest, often using correlations, regression or relative-importance methods. Product and customer-experience teams use it to identify candidate priorities and develop hypotheses about the experience.

Specify the outcome and the candidate explanations

In a fictional delivery survey, the outcome might be overall satisfaction and the predictors might include timeliness, communication and problem resolution. Those measures need clear wording and a reason for inclusion. A model cannot assess an important factor the questionnaire never captured.

Tools use different calculations under the same label. Qualtrics’ key-driver documentation describes correlation and relative-weight approaches. Check which method produced the ranking and what its values mean before presenting them as comparable measures of influence.

Highly related predictors can make attribution difficult. Customers who rate delivery timeliness highly may also rate reliability highly, leaving the model with overlapping information. The resulting ranking can depend on which variables were included and the method used to distribute shared explanatory value.

Check the model before turning ranks into priorities

Use an analysis appropriate to the outcome and data structure, and examine uncertainty, influential observations and fit. A binary retention outcome calls for different modelling assumptions from a continuous score. Repeated responses and account-level clustering may also need explicit treatment.

Avoid reading the largest coefficient as the most important factor without considering scale and coding. A one-unit difference in days is not the same as a one-point difference on a rating scale. Relative-importance measures answer their own defined questions and still need context.

Use the result to sharpen the next investigation

An association may reflect a causal effect, a common influence or the way respondents use the questionnaire. Adding controls does not automatically remove every alternative explanation. Correlation versus causation remains central to interpreting the result.

Compare candidate priorities with the actual experience, the room for improvement and the practical consequences of a change. User interviews can explore what “reliable delivery” means, while an appropriate experiment may evaluate an intervention.

Report a ranked set of associations with uncertainty and limitations, rather than a guaranteed recipe for raising the headline score. The analysis helps identify where to look more closely; it does not replace the work of understanding and testing the proposed improvement.