Goal-oriented models have become important tools for the analysis of non-functional requirements (NFRs). However, the treatment of NFRs is a non-trivial task, considering that this class of requirements covers quality characteristics. This implies that when dealing with subjective requirements, we need to focus on mechanisms that can enrich the semantics of their representation. This is the case of assigning and propagating labels in the evaluation of goal-oriented models. The definition of labels in the goal models, carried out by the existing qualitative analysis methods, has low granularity and often fails to reflect the information potential that this type of artifact may offer. This is the case of the NFR Framework. Propagation in the model is bottom-up and understanding the degree of goal satisficing becomes difficult. This paper explores a rationale to increase the informative power of the labels assigned to the goals, using the concepts of colorimetry in the SIG (Softgoal Interdependency Graph). We discuss how color mitigates the challenge of increasing the granularity of goal models analysis, thus improving the evaluation of these models.