Predicting Customer Class using Customer Lifetime Value with Random Forest Algorithm

Abstract

As there are a lot of booming online retailers in e-commerce industry in the Internet age, the need of maintaining competitive advantages has become to pay attention to customer relationship management (CRM). To build a successful CRM strategy, it is needed to know individual customer class which can be calculated from Customer Lifetime Value (CLV): the monetary value of customers purchased from the business during their lifetime. CLV modelling allows us to identify customer's predicted business value. It provides the retailers for effectively allocating the resource in their business. This predictive model has been taken on the global Super Store Retail dataset with almost ten thousand transactions. Our model will predict the customers' class of the next year based on their CLV that will help the online retailer to decide which customer should be invested to get long term CRM. Random Forest (RF) algorithm is utilized to train our model and Random Search tuning is conducted to get the best predictive accuracy. The experimental analysis is performed to compare with AdaBoost algorithm on the same dataset.