One starting point to reduce harmful greenhouse gas emissions is driving behavior. Previous studies have already shown that eco-feedback leads to reduced fuel consumption. However, less has been done to investigate how driving behavior is affected by ecofeedback. Yet, understanding driving behavior is important to target personalized recommendations towards reduced fuel consumption. In this paper, we investigate a real-world data set from an IoT-based smart vehicle service. We first extract seven distinct factors that characterize driving behavior from data of 5,676 users. Second, we derive initial hypotheses on how eco-feedback may affect these factors. Third, we test these hypotheses with data of another 495 users receiving eco-feedback. Results suggest that ecofeedback, for instance, reduces hard acceleration maneuvers while interestingly speed is not affected. Our contribution extends the understanding of measuring driving behavior using IoT-based data. Furthermore, we contribute to a better understanding of the effect of eco-feedback on driving behavior.