Predicting Customer Churn Rates in Service Providing Organizations Using Riemann Residual Neural Network with Snow Geese Algorithm

Customer churn is a significant challenge businesses face when customers abandon or switch to other service providers. Churn may also be as a result of the dissatisfactory service or as a result of increasing prices within the service industry. The churn rate represents one of the biggest strategic concerns for the providers of services, thus they should design a proactive approach to churn which means that churn should be recognized and, if possible, prevented due to the main reasons for dissatisfaction. It was noted that even 1 percent increase in customer retention rate implies relatively significant increase in net present value in the majority of service providing organizations. The main objective of this effort is to build a churn prediction model that will enable the telecom employees to understand which customers are probable to churn. The Hybrid Mountain Gazelle Optimizer with Fire Hawk Optimization (HMG-FHO+) algorithm is used in the experimental strategy for this study to choose relevant features from the large dataset for the churn dataset. Riemann Residual Neural Network (RieRes-NN) paired with the Snow Geese Algorithm for optimization is used to measure the model's performance. The analysis shows that RieRes-NN has a very high capacity of predicting the outcomes with a precision of 93%. About 88% compared to the standard network. The combined RieRes-NN-SG method results in higher accuracy of churn predictions and allows for creating more effective concrete actions to minimize churn rates and increase clients' satisfaction in an organization. Our methodology described here is a major contribution of predictive analytics plus it offers a robust instrument for preventive handling of customers in service-delivery organizations.