Customer Relationship Management (CRM) has been an important part of enterprise decision-making and management. In this regard, Decision Tree (DT) models are the most common tools for investigating CRM and providing an appropriate support for the implementation of CRM systems. Yet, this method does not yield any estimate of the degree of separation of different subgroups involved in analysis. In this research, we compute three decision-making models in SMEs, analyzing different decision tree methods (C&RT, C4.5 and ID3). The methods are then used to compute ME and VoE for the models and they were then used to calculate the Mean Errors (ME) and Variance of Errors (VoE) estimates to investigate the predictive power of these methods. These decision tree methods were used to analyze small- and medium-sized enterprises (SME’s) datasets. The paper proposes a powerful technical support for better directing market tends and mining in CRM. According to the findings, C&RT shows a better degree of separation. As a result, we recommend using decision tree methods together with ME and VoE to determine CRM factors.