The increasing connectivity and data exchange between vehicles and the cloud have led to growing privacy concerns. To keep on gaining product insights through data collection while guaranteeing privacy protection, an anonymization-by-design approach should be used. A rising number of anonymization methods, not limited to the automotive domain, can be found in the literature and practice. The developers need support to select the suitable anonymization technique. To this end, we make the following two contributions: 1) We apply our knowledge from the automotive domain to outline the usage of qualitative metrics for anonymization techniques assessment; 2) We introduce HySAAD, a hybrid selection approach for anonymization by design that leverages this groundwork by recommending appropriate anonymization techniques for each mobile data analytics use case based on both, qualitative (i.e., ßoft") metrics and quantitative (i.e., "hard") metrics. Using a real-world use case from the automotive, we demonstrate the applicability and effectiveness of HySAAD.