The company needed to foresee customer churn to enable proactive marketing efforts and improve business performance. By developing a high-accuracy predictive model, churn was reduced, and marketing effectiveness increased.
High customer churn led to a decrease in energy demand and a reduction in revenue. Additionally, there was a need to predict customer churn well in advance to give marketing and customer service teams enough time to react.
A machine learning model was developed to predict customer churn risk and enable targeted marketing efforts. The approach included model development and training, deployment, and alignment with marketing strategies to enhance customer retention.
