This project aims to use machine learning techniques to segment and profile mobile gamers in terms of their in-game spending. Estimates suggest that more than 2.6bn people play mobile games globally; that more than 80 billion mobile games are downloaded annually; and that mobile gaming accounts for almost $100bn in transactions every year. 

Despite the profitability of mobile gaming, little is known about how different kinds of players spend money in mobile games. Informal theories regarding specific differences in gaming are widely espoused: one influential model, for example, posits the existence of a small but profitable layer of heavily-involved 'whales', and much larger groups of smaller-spending 'dolphins' and 'minnows'. However, it is unclear whether this structure really does explain the monetisation of most games; and whether monetisation may vary between games; and between cultural contexts. 

In this project, we will take a data-driven approach, and apply a variety of machine learning techniques to large datasets of real player transactions. By both applying and developing algorithmic techniques for the analysis of such data, we will help build an understanding of how in-game spending may be profiled. 

This project would suit a machine learning specialist; a quantitative social scientist, or a data scientist wishing to do impactful work. It will be supervised by David Zendle, one of the world's leading experts on video game monetisation, and may involve one or more industrial partners who will share player data for the project.