  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.
