A Meta-learning based Stacked Regression Approach for Customer Lifetime
  Value Prediction
Karan Gadgil Sukhpal Singh Gill Ahmed M. Abdelmoniem
Companies across the globe are keen on targeting potential high-value
customers in an attempt to expand revenue and this could be achieved only by
understanding the customers more. Customer Lifetime Value (CLV) is the total
monetary value of transactions/purchases made by a customer with the business
over an intended period of time and is used as means to estimate future
customer interactions. CLV finds application in a number of distinct business
domains such as Banking, Insurance, Online-entertainment, Gaming, and
E-Commerce. The existing distribution-based and basic (recency, frequency &
monetary) based models face a limitation in terms of handling a wide variety of
input features. Moreover, the more advanced Deep learning approaches could be
superfluous and add an undesirable element of complexity in certain application
areas. We, therefore, propose a system which is able to qualify both as
effective, and comprehensive yet simple and interpretable. With that in mind,
we develop a meta-learning-based stacked regression model which combines the
predictions from bagging and boosting models that each is found to perform well
individually. Empirical tests have been carried out on an openly available
Online Retail dataset to evaluate various models and show the efficacy of the
proposed approach.