Recent advances in eXplainable Artificial Intelligence (XAI) led to many different methods in order to improve explainability of deep learning algorithms. With many options at hand, and maybe the need to adapt existing ones to new problems, one may find in a struggle to choose the right method to generate explanations. This paper presents an objective approach to compare two different existing XAI methods. These methods are applied to a use case from literature and to a real use case of a French administration.