<p>Deep Learning (DL) forecasts Customer Lifetime Value (CLV) and optimises CRM in current research. ML models can be adapted and used alongside CRM methods to recognise customer behaviour anomalies amid numerous customer relationships, heterogeneous statistics, and time-sensitive data. This technique allows companies to maintain customers and improve profit, advertising, and confidence, divided by income. First, the study recommends a multi-output Deep Neural Network (DNN) model for predicting CLV. The suggested framework was measured with multi-output Decision Tree (DT) and multi-output Random Forest (RF) techniques on the same dataset. The study presents a multilayer supervised DL-based CLV prediction technique that enhances features on limited data, outperforming better-quality goods in marketing effectiveness and client lifetime value. The research explores using CLV prediction in personalized customer experiences, highlighting its potential to enhance CRM strategies by incorporating dynamic variables and current data for improved accuracy. The Deep Neural Network model has an acceptable error rate of MAPE of 10.3%, MSE of 11.6%, and RMSE of 12.29%, demonstrating reasonable complete error rates.</p>
Authors: Nabeel S. Alsharafa, P. Madhubala, Sree Lakshmi Moorthygari, K. N. Rajapraveen, B. Rajesh Kumar, Sudhakar Sengan, Pankaj Dadheech
DOI: https://doi.org/10.32629/jai.v7i5.1622
Publish Year: 2024
