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

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dc.contributor.author Sifa, Rafet
dc.contributor.author Runge, Julian
dc.contributor.author Bauckhage, Christian
dc.contributor.author Klapper, Daniel
dc.date.accessioned 2017-12-28T00:42:26Z
dc.date.available 2017-12-28T00:42:26Z
dc.date.issued 2018-01-03
dc.description.abstract 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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2018.115
dc.identifier.isbn 978-0-9981331-1-9
dc.identifier.uri http://hdl.handle.net/10125/50002
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data, Text and Web Mining for Business Analytics
dc.subject Behavioral Analytics, Customer Lifetime Value Prediction, Digital Marketing, Rarity Mining, User Recommender Systems
dc.title Customer Lifetime Value Prediction in Non-Contractual Freemium Settings: Chasing High-Value Users Using Deep Neural Networks and SMOTE
dc.type Conference Paper
dc.type.dcmi Text
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