A 360° customer lifetime value prediction method using machine learning for multi category e-commerce companies

The Customer Lifetime Value (CLV) prediction methods that are used by e-commerce companies are mainly focusing on a specific group of customers with multiple transaction data and therefore remain unable to value the one-time purchasers. As a result, the management of these companies is incapable of valuing all customers and the overall company. Thus, each type of user needs a different way to predict CLV. In this thesis, we intend to develop a novel 360° holistic technique for the prediction and use of CLV models in the marketing management of multi-category e-commerce companies to enhance their strategic decision-making. The research compared the proposed framework which was constructed with several outputs (CLV, DPC, and TAS) with other ML models to evaluate the new variables created based on relationship marketing theory (RMT) and to demonstrate the best model which is more appropriate for multi-category e-commerce companies' usage. To make this result useful, we created customer clusters that enable management to separate end-users according to the three outputs. Finally, Shapley values obtained using explainable artificial intelligence (XAI) are then utilized to understand the DNN's findings. The results showed that using XAI shows which factors are more crucial to the results. Overall, the proposed model helps marketing management teams in planning their operations efficiently by differentially allocating their resources to specific types of customers based on their profitability which provides more strategic decision-making.