Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE

Sifa, Rafet
Runge, Julian
Bauckhage, Christian
Klapper, Daniel
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In non-contractual freemium and sharing economy settings, a small share of users often drives the largest part of revenue for firms and co-finances the free provision of the product or service to a large number of users. Successfully retaining and upselling such high-value users can be crucial to firms' survival. Predictions of customers' Lifetime Value (LTV) are a much used tool to identify high-value users and inform marketing initiatives. This paper frames the related prediction problem and applies a number of common machine learning methods for the prediction of individual-level LTV. As only a small subset of users ever makes a purchase, data are highly imbalanced. The study therefore combines said methods with synthetic minority oversampling (SMOTE) in an attempt to achieve better prediction performance. Results indicate that data augmentation with SMOTE improves prediction performance for premium and high-value users, especially when used in combination with deep neural networks.
Data, Text and Web Mining for Business Analytics, Behavioral Analytics, Customer Lifetime Value Prediction, Digital Marketing, Rarity Mining, User Recommender Systems
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