When should I transform the predictors?

A regression analysis usually assumes that predictors are normally-distributed. If such assumption is violated, it is usually a good idea to transform the predictors using a Box-Cox transformation.

In marketing, many predictors, such as amounts or purchased frequencies, are naturally right-skewed.

For instance, in a typical customer database, many customers will have made one or two purchases at most, whereas only a few will have made a very large number of purchases. Since the first initial purchases contain much more predictive power than later ones (i.e., there is a huge difference between 1 and 2 purchases, but very little between 36 and 37), transforming the predictors might significantly improve the model performance.