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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.

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Mobile digital games are dominantly released under the freemium business model, but only a small fraction of the players makes any purchases. The ability to predict who will make a purchase enables optimization of marketing efforts, and tailoring customer relationship management to the specific user's profile. Here this challenge is addressed via two models for predicting purchasing players, using a 100,000 player dataset: 1) A classification model focused on predicting whether a purchase will occur or not. 2) a regression model focused on predicting the number of purchases a user will make. Both models are presented within a decision and regression tree framework for building rules that are actionable by companies. To the best of our knowledge, this is the first study investigating purchase decisions in freemium mobile products from a user behavior perspective and adopting behavior-driven learning approaches to this problem.

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Some companies offering online services employ tactics that make it hard for customers to quit their accounts. These tactics are commonly referred to as “dark patterns” and may include hiding the cancelation procedure, asking customers to go through an excessive number of steps to complete the cancelation, or simply not letting customers quit their accounts straight away. Arguably, dark patterns are the result of misaligned incentives between companies and customers as companies can still benefit from their customers’ data even if they no longer use the companies’ services. Against this background, the authors conduct an observational survey of the state of current market practice and call for future research that enhances our understanding of dark patterns, their organizational antecedents, customers’ psychological responses to these tactics, and the wider consequences of dark patterns for firms and markets.

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