Deep Learning Models for Customer Lifetime Value Prediction in E-commerce

Responding to the ever-changing e-commerce scene by utilising deep learning models for CLV forecasting in a
variety of contexts. Client lifetime value is an important KPI for companies since it shows how much money customers are projected to spend while they are a part of a company's
network. Improved CLV model accuracy and predictive
capacity are the goals of this research, which use deep
learning techniques—specifically, neural networks. In order
to understand how consumer data contains temporal linkages
and how these links affect purchasing behaviour, this article will look at several architectures like LSTMs and RNNs. The has two goals: first, to improve CLV estimates by analysing past transactions in detail; and second, to provide useful information for developing targeted marketing campaigns and retention-oriented programmes. The outcomes help bring e-commerce analytics and deep learning together.