The optimal situation to make a decision is to have all variables in grasp. This however, almost never occurs. There has been research on counterfactuals as a way to provide more explainable systems and models. In furtherance of this research, this paper proposes CORFAD, Counterfactual Retrieval for Augmentation and Decisions. We explore user generated counterfactual tweets and by aggregating counterfactual statements that relate to pre-determined keywords, CORFAD simplifies data analysis by suggesting variables towards which future actions might have the greater or lesser effects towards a defined goal. This has the dual purpose of making synthetic counterfactual data generation more focused and less likely to generate non-useful explanations, while also able to stand alone to assist decision makers. This paper uses as test case, Counterfactual Statements connected with the Tesla Model 3 to explore insights that can guide decision-making in situations where multiple variables are possible and exist.