"To accurately define and measure performance is a complex process for most businesses, yet crucial for optimal distribution of company resources and to accomplish alignment across business units. Despite the large amount of data available to most modern companies today, performance metrics are commonly selected based on expertise, tradition, or even gut-feeling. In this thesis, a data-driven approach is proposed in the form of a statistical framework for evaluating and selecting performance metrics. The outline of the framework is influenced by the method of time series feature selection and wraps the search for relevant features around a time series forecasting model. The framework is tuned by experiments exploring state-of-the-art forecasting models, in combination with two different feature selection methods. The results demonstrate that for metrics similar to the real-world data used in this thesis, the best framework incorporates the filter feature selection method in combination with en univariate time series forecasting model."