Customer Lifetime Value Analysis Based on Machine Learning

Abstract

Customer lifetime value (CLV) is a powerful tool to determine the value of customers and filter customers most likely to attrite or most likely to make their first purchase, especially for e-commerce companies. This article reviewed machine learning models in analyzing CLV and prospected some potential directions for future research. Data of 8099 samples were collected and analyzed through four kinds of machine learning methods: Linear Regression, Support Vector Machine, Random Forest, Neural Network. The correlations between features showed that CLV are generally affected by monthly premium auto, total claim amount, and coverage. Analysis through machine learning models has high precision and Random Forest performs best. CLV prediction and customer segmentation are vital in business field today. Marketers could take advantage of the huge amount of data and machine learning models to portrait customer behaviors. Collecting browsing and purchase histories is also beneficial for providing best offers to individual customers.