I guess you want to compare a linear SVM to your Logistic Regression. (Otherwise it's not fair )
Basically there is only one parameter, that you can use for tuning the linear SVM, it is the penalty value (C).
SVMs do not outperform Logistic Regression in every situation.
They are stable, work very well with high dimensional data...
SVMs are maximal margin classifiers. They work the best if there is a margin. In business terms this means your event and non-event cases are very different. There are only a few "in the middle".
Logistic Regression assumes a smooth transition of the probability as features change.
Don't forget to compare your models on an independent test set.