As marketplaces have become increasingly crowded, businesses have recognized the importance of focusing their business strategy on identifying customers who are likely to leave their services. To solve this, a technique for identifying these consumers must launch pre-emptive retention efforts to keep them. Therefore, to minimize costs and maximize efficiency, churn prediction must be as precise as possible to guarantee retention efforts are directed solely at customers who intend to transfer service providers. The study conducted in this report aims to establish a mechanism for anticipating churn in advance while minimizing misclassification. The suggested methodology integrates a temporal dimension into customer churn prediction to maximize future attrition capture by identifying probable customer loss as soon as possible. Six machine learning algorithms are selected and conducted to validate the suggested methodology using a bank credit card dataset. Finally, the proposed methodology’s results are compared to those published churn prediction methodologies. According to the research, clients can be classified into clusters based on their contracts with the service provider. It is conceivable to estimate when the customer might be expected to end their service with the organization.
|Title of host publication||2022 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)|
|Number of pages||9|
|Publication status||Published - 23 Mar 2023|
|Event||2022 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC) - Beijing, China|
Duration: 23 Sept 2022 → 25 Sept 2022
|Conference||2022 International Conference on Industrial IoT, Big Data and Supply Chain (IIoTBDSC)|
|Period||23/09/22 → 25/09/22|