Correlation Analysis is generally used to look at associations between different variables. Usually, we look for linear associations – Pearson correlations. In some cases, we may look for correlations between ranks of different variables in which case the non-parametric Spearman Rank Correlation may be used. Bivariate correlation analysis is essentially exploratory in nature though some market researchers have used this for comparison of key-drivers. In a key driver analysis with multiple impact variables, bivariate correlations make for unreliable judgments on the importance of the variables since in each bivariate correlation, the effect of the other impact variables is ignored.
Regression analysis with multiple impact variables is a more reliable method of assessing and quantifying impact of a set of independent variables on a focus dependent variable. Quantifies estimates of impact in absolute or percentage terms are provided by the beta-coefficients under a ceteris-paribus assumption.
Proper understanding of MR and analytics helps us avoid either over specification or under-specification errors in regression modeling. After all, the impact of a variable in a regression equation actually depends on the other variables present in the model. Including too many or too few variables is itself a problem and proper variable selection must precede actual estimation.