Estimating Potential Customer Churn in Industries Employing Deep Neural Network Model

Estimating Potential Customer Churn in Industries Employing Deep Neural Network Model

Seema Mandloi1, Prof. Virendra Verma2

ABSTRACT

Data Science and Machine Learning are being used extensively for business analytics. One of the major applications happens to be estimating churn and attrition rates. In today’s competitive market landscape, retaining customers is as crucial as acquiring new ones. Churn rate, which measures the proportion of customers who discontinue their relationship with a business over a specific period, is a critical metric for companies across industries. Forecasting churn enables businesses to proactively address customer dissatisfaction and refine their strategies to retain valuable clients. By understanding the likelihood of churn, companies can make informed decisions to sustain growth and profitability. The proposed approach combines swarm intelligence and neural networks to forecast churn rates. The results clearly indicate that the proposed approach outperforms existing baseline approaches in terms of forecasting  accuracy.

Keywords: Data Analytics, Machine Learning, Churn Rate, Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Mean Absolute Percentage Error, Regression.