Abstract
As game companies embrace increasingly a service oriented business model, the need for predictive models of player behaviour becomes more pressing. Various 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 behaviour make predictions about the revenue and allocation of budget and resources extremely challenging.
In this chapter we will present and overview of customer lifetime value modelling 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.
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 behaviour make predictions about the revenue and allocation of budget and resources extremely challenging.
In this chapter we will present and overview of customer lifetime value modelling 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.
| Original language | English |
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| Title of host publication | Data Analytics Applications in Gaming and Entertainment |
| Publisher | CRC Press |
| Publication date | 18 Jun 2019 |
| Chapter | 5 |
| ISBN (Print) | 978-1138104433 |
| Publication status | Published - 18 Jun 2019 |
| Series | Data Analytics Applications |
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