Customer churn extends beyond a mere indicator of revenue loss: across diverse industries, it poses a formidable challenge that can profoundly destabilize a business's foundation. It undermines long-term strategic planning, escalates operational costs, and frequently signals underlying deficiencies, such as product quality or customer service efficiency. Against this background, predictive analytics has transitioned from a desirable addition to an indispensable element of business strategy.
Historically, this domain has leaned on traditional statistical models, including logistic regression and decision trees. These methodologies sift through historical customer data to identify indicators predictive of future service discontinuation. Although these methods have demonstrated resilience over time, their adequacy is increasingly being questioned. In this regard, ensemble learning emerges as a sophisticated alternative, offering enhanced precision and reliability in identifying potential customer attrition.