Machine Learning Algorithms for Predicting Customers' Lifetime Value: A Systematic Evaluation
- Resource Type
- Conference
- Authors
- Kailash, Harshit; Kanwar, Kushal; Sonia, Sonia; Kant, Ravi
- Source
- 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) Advance Computing and Innovative Technologies in Engineering (ICACITE), 2023 3rd International Conference on. :538-541 May, 2023
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Support vector machines
Training
Machine learning algorithms
Systematics
Linear regression
Customer relationship management
Predictive models
- Language
With so many successful online merchants in the e-business market in the online era taking heed to customer relationship management (CRM) has become a prerequisite for maintaining competitive advantage. Single customer categories that can be measured in Customer Lifetime Value (CLV) which is defined as the quantity of earnings from a customer in a business during their lifetime, is required to develop an effective CRM strategy. CLV modelling using four most popular Machine Learning(ML) algorithms namely linear regression, decision tree, support vector machine and random forest algorithm are used to find the expected business estimate of a client. The IBM Watson Dataset was used as a training set to evaluate and compare the performance of the four ML regressors in terms of key parameters such as RMSE and R Squared. The findings in this research offer a summary of cutting edge ML methods for CLV prediction.