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Masood, Safa (2024) Predicting Sales and Analysing Customer Lifetime Value (CLV) in the E-Commerce Industry Using Machine Learning Methods. Masters thesis, Dublin, National College of Ireland.
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Abstract
The advancement of e-commerce has brought a major change in the business sector by allowing companies to connect with customers globally and offer different products and services. To manage an e-commerce business prediction of sales and calculating the Customer Lifetime Values are the pivotal components in formulating the strategy, customer differentiation, and utilization of resources. However, the data produced by e-commerce such as customer interactions, buying and selling transactions, and market forces of demand and supply form a real challenge to extract meaningful information and intelligence. Another challenge is the heterogeneity of the customers as e-commerce firms are dealing with customers from different backgrounds. It is important to anticipate such conduct for customizing the marketing, maintaining the clients, and optimization of supplies. However, it is crucial to find such customers who are worthy of constant revenue generation called Customer Lifetime Values (CLV). To address these challenges, the research focuses on analyzing the suitability of various regression algorithms in predicting sales and analyzing the customer lifetime value (CLV) in the context of e-commerce. The objective of this research includes the implementation of various regression-based machine algorithms for forecasting sales based on the Brazilian e-commerce dataset. After our analysis, we have identified Random forest as the most accurate model for predicting sales with a minimum error score. The accurate prediction of Sales and analysis of CLV in our research allows optimization of the acquisition of customers, retention, and overall profitability for the business.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Onwuegbuche, Faithful UNSPECIFIED |
| Subjects: | H Social Sciences > HF Commerce > Customer Service H Social Sciences > HF Commerce > Electronic Commerce H Social Sciences > HG Finance > Fintech T Technology > T Technology (General) > Information Technology > Fintech Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in FinTech |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 05 Aug 2025 10:49 |
| Last Modified: | 05 Aug 2025 10:49 |
| URI: | https://norma.ncirl.ie/id/eprint/8427 |
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