Customer Lifetime Value Forecasting Using Ensemble Learning on E-commerce Big Data

For e-commerce companies, the Customer Lifetime Value (CLV) is an important metric, and it is used to make decisions about customer achievement, maintenance, and resource allocation. For any company, especially a business in a competitive market, its CLV's accuracy in forecasting plays a vital role in defining whether or not it remains profitable and a favorite in its market or if it loses the market share. This paper suggests methods to increase the accurateness of the CLV estimates over collective learning methods such as Gradient Boosting, Random Forest, and Voting Classifier using large-scale e-commerce data. The ensemble methods association the assets of individual models, decrease overfitting, and enhance predictive power, especially when the complex and high-dimensional customer behavior data. Experimental results are presented that show that the Voting Classifier outperforms conventional single-model currency exchange rate prediction strategies, which obtain better predictive accuracy with lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). It provides useful ideas to e-commerce enterprises on how to segment customers, carry out targeted marketing, and allocate resources effectively. It also introduces the ethical considerations that are related to data privacy, security, fairness, and ethical use of customer data. The findings provide further knowledge on machine learning applications in customer analytics and provide a robust solution framework to improve CLV forecasting in a dynamic e-commerce environment.