This design research builds on the idea to combine the strengths of traditional survey research with a more practice-oriented benchmarking approach. We present, an online survey platform that allows providing instant and respondent-specific feedback based on a scientifically grounded research model and a structural equation model-based prediction technique. Based on the partial least squares analysis results of a training dataset, selfsurvey employs a scoring algorithm to derive respondent-specific predicted scores, compares these with the observed scores, and provides visualized and text-based outputs. Our evaluation of selfsurvey in the context of a maturity benchmarking study provides an indication for the perceived usefulness of this artifact and its underlying scoring algorithm. We argue that this prediction-based approach, which goes far beyond the functionality of common univariate benchmarking tools, can be used for a wide range of survey studies and help increase the perceived relevance of academic survey studies to practice.