Customer Risk Prediction: A Machine Learning Ensemble Approach

In order to understand the links between customer behaviour and growth, researchers and consultants have recently developed several customer metrics. Although metrics like NPS (Net Promoter Score) and Client Sentiment Scores attempt to capture certain customer behaviours, several prominent and widely used indicators of customer health have only shown modest correlations with growth. These metrics are unable to provide a comprehensive view of customer health and lack predictive capacity. Early customer metrics attempted to address consumer motivations; however, the issues that impact consumer loyalty are intricate and go beyond conventional and more general risk metrics. This paper introduces a new methodology for predicting customer risk, focusing on comprehending the important risk factors and providing a holistic overview. The procedure involves gathering and organizing pertinent data that captures various risk factors and features, analysing it, and employing machine learning methods. Once verified, the model is implemented to generate predictions for new client data, assisting businesses in making informed decisions and taking proactive steps to establish stronger client relationships.