Abstract Telecom industry, being a dynamic and competitive environment inherently exhibits high potential for customer churn. In such a dynamic scenario, conventional performance metrics fail to reflect business goals effectively. This is due to the misalignment existing between the business goals and performance metrics. This research proposes a heterogeneous telecom churn prediction (HESMU) model enhanced by minority elevation strategies, with which data imbalance can be handled effectively and also proposes a loss function that bifurcates loss as a function of unavoidable loss and loss due to the wrong prediction. HESMU is a two-stage process; the initial stage performs first level predictions on the training data using multiple heterogeneous base learners and the second stage performs minority upliftment from the predictions generated by the initial models. The initial stage is composed of Gradient Boosted Trees and Naive Bayes, while the second combiner stage is based on One-Class SVM. Experimental results reveal that the HESMU model shows 1%–7% higher churn prediction levels and 1.3–1.7 times reduced loss levels when compared to existing classifier models and the state of the art churn prediction techniques from literature.