Interpreting coefficients after transforming Y (dependent) variable
By using BoxCox transformation on the Y variable, you are turning your model from linear regression into non-linear regression. Note that although it seems you are training a linear regression, it’s only linear with respect to the transformed version of Y but non-linear regarding the original Y. Therefore you can not expect a linear behaviour from a non-linear model which means you can not work out the actual sales change resulting in changes in X1 and X2. That’s because unlike linear models, in non-linear models f(x+b) is not equal to f(x) + f(b)