In the innovative industry, four major trends are found to influence product quality and reliability: the increase in product complexity, the strong pressure on time to market, the increasing global economy, and the decreasing tolerance for quality problems. Thus, it becomes more difficult to anticipate all potential failures during the product development process. In this context, an efficient field feedback process should be in place to react to the unanticipated deviations in product performance. Based on a case study made in an innovative company, this paper shows that the problem is not so much in the information collection as in the inherent quality of the information and in the manner the information is processed. Therefore, a new method, presented in this paper, was developed to classify and prioritize field data and to upgrade it into information that can be used for design improvement according to the dominant classes of failures using the four-phase roller coaster model. Although this newly generated information is richer than raw field data it is not yet detailed enough to allow direct design optimization. Therefore, a second upgrading stage, based on design of experiments, was developed. It uses a method that combines physics-of-failure (bottom-up) and field information (top-down). As traditional DoE mainly deals with largely time-independent quality data obtained during the manufacturing process the approach had to be modified to deal with time-dependent reliability data. Case study results show that it is a promising approach for characterizing and resolving failure mechanisms also in innovative companies.