Counterfactual explanations play a crucial role in interpreting and understanding the decision-making process of complex machine learning models, offering insights into why a particular prediction was made and how it could be altered. However, individual counterfactual explanations generated by different methods may vary significantly in terms of their quality, diversity, and coherence to the black-box prediction. This is especially important in financial applications such as churn analysis, where customer retention officers could explore different approaches and solutions with the clients to prevent churning. The officer’s capability to modify and explore different explanations is pivotal to his ability to provide feasible solutions. To address this challenge, we propose an evaluation framework through the implementation of an ensemble approach that combines state-of-the-art counterfactual generation methods and a linear combination score of desired properties to select the most appropriate explanation. We conduct our experiments on three publicly available churn datasets in different domains. Our experimental results demonstrate that the ensemble of counterfactual explanations provides more diverse and comprehensive insights into model behavior compared to individual methods alone that suffer from specific weaknesses. By aggregating, evaluating, and selecting multiple explanations, our approach enhances the diversity of the explanation, highlights common patterns, and mitigates the limitations of any single method, offering to the user the ability to tweak the explanation properties to their needs.