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Companies across the globe are keen on targeting potential high-valuecustomers in an attempt to expand revenue and this could be achieved only byunderstanding the customers more. Customer Lifetime Value (CLV) is the totalmonetary value of transactions/purchases made by a customer with the businessover an intended period of time and is used as means to estimate futurecustomer interactions. CLV finds application in a number of distinct businessdomains such as Banking, Insurance, Online-entertainment, Gaming, andE-Commerce. The existing distribution-based and basic (recency, frequency &monetary) based models face a limitation in terms of handling a wide variety ofinput features. Moreover, the more advanced Deep learning approaches could besuperfluous and add an undesirable element of complexity in certain applicationareas. We, therefore, propose a system which is able to qualify both aseffective, and comprehensive yet simple and interpretable. With that in mind,we develop a meta-learning-based stacked regression model which combines thepredictions from bagging and boosting models that each is found to perform wellindividually. Empirical tests have been carried out on an openly availableOnline Retail dataset to evaluate various models and show the efficacy of theproposed approach.

A Meta-learning based Stacked Regression Approach for Customer Lifetime Value Prediction