Logistic Regression and SVM often give the similar results. SVM costs longer to train than logistic regression, so it seems that there is no obvious reason to use SVM. Actually, in industry logistic regression is the most frequently used algorithm.
The reason that logistic regression and SVM have similar performance is that the training data is linearly separable, which happens very often. Therefore, there is no need to project the value to a higher dimension to separate them.
Under some situations, SVM may perform much better than logistic regression. For example, when training sample size is relatively small and there are a lot of features. In this case, if the dataset is not linearly separable SVM may lead to better performance.