Predicting Customer Lifetime Value in Free-to-Play Games
This addresses the need for predictive models to support user acquisition and operations in free-to-play games, but it is incremental as it reviews existing methods rather than proposing new ones.
The paper tackles the problem of predicting customer lifetime value in free-to-play games, where erratic player behavior makes revenue and resource allocation challenging, by providing an overview of existing models and state-of-the-art solutions with practical examples.
As game companies increasingly embrace a service-oriented business model, the need for predictive models of players becomes more pressing. Multiple activities, such as user acquisition, live game operations or game design need to be supported with information about the choices made by the players and the choices they could make in the future. This is especially true in the context of free-to-play games, where the absence of a pay wall and the erratic nature of the players' playing and spending behavior make predictions about the revenue and allocation of budget and resources extremely challenging. In this chapter we will present an overview of customer lifetime value modeling across different fields, we will introduce the challenges specific to free-to-play games across different platforms and genres and we will discuss the state-of-the-art solutions with practical examples and references to existing implementations.