Rapidly growing competitive organizations, especially telecommunication industry, face customer churn challenges. It implies customers who leave services or companies and switch to the next competitors. The acquisition of new customers costs the telecommunication sector more than keeping existing customers. Therefore, in the dynamic landscape of the telecommunication industry, understanding and effectively segmenting customers based on their behavior is paramount for personalized service delivery and improved business outcomes. Existing approaching considering customer churn problem as binary class problem; however, it cannot handle hard-topredict borderline churn, and thus usually avoids these instances during the model training process to reduce the variance errors. Therefore, there is a need to close the gap by implementing an approach not only to classify the customers into churn and non-churn classes, but it can also efficiently segment the hard-to-predict borderline customer churn. This talk will provide a brief overview of an adaptive customer segmentation method using customer portfolio analysis to uncover hidden patterns and behaviors within the customer base.