Customer Churn Prediction using Sandpiper Optimization with Bidirectional Gated Recurrent Unit for Business Intelligence

Abstract

Recent innovation in business intelligence (BI) assists companies to stay successful and competitive with the increasing business trend. Businesses have started to examine the succeeding level of data analytics and BI solution. At the same time, Customer Churn Prediction (CCP) is an essential procedure involved in business decision making that effectually determines the churn clients and performs adequate processes to retain customers. With this motivation, this paper presents a sandpiper optimization with bidirectional gated recurrent unit (SPO-BiGRU) for CCP on BI applications. The SPO-BiGRU model aims for determining the occurrence of customers into churner or non-churner. In addition, the SPO-BiGRU technique involves pre-processing, classification, and hyperparameter optimization. Followed by, the BiGRU model is applied to perform the predictive process. At last, the SPO algorithm is applied to optimally adjust the hyperparameters involved in the BiGRU model. For validating the enhanced performance of the SPO-BiGRU method, a wide range of simulations take place and the results are inspected under varying aspects. The experimental results portrayed the supremacy of the SPO-BiGRU technique over the recent state of art approaches.