Churn Prediction for Rail Nation
Game development is increasingly relying on data analytics. Among the many use cases, predicting and modelling player churn – i.e. predicting players that will leave or stop playing at some point – based on tracked in-game data has become of key interest. This is especially true for new business models that rely on the continued engagement of players such as subscription-based services or free-to-play games where monetization is, e.g., happening through micro-transactions. Customer churn prediction can thus be pivotal for the success of games.
The goal of this thesis is to develop a churn predication model for the round-based, free-to-play, online strategy game Rail Nation from Travian Games. In the game, players build up a railway company and manage transports to increase their prestige and wealth in order to achieve victory over the competitors. A game round is scheduled to last between 3 – 4 months. It consists of six consecutive railway eras followed by an endgame, in which all players compete with their grown railway companies against each other in the ultimate battle.
The thesis will be conducted in close collaboration with Travian Games who will also provide access to the game and in-game data. Travian Games is located in Munich and focused on developing and marketing PC and browser games.
Your job is to develop a churn prediction model for the Classic Mode of Rail Nation based on tracked in-game behavioral data (possibly in connection with survey data). Due to the large number of available metrics this will likely include selecting an appropriate subset of the metrics. The churn prediction model should be useable for real-time monitoring and be complemented with a browser-based visual dashboard to visualize data from the churn model. The model should be implemented in Python and/or R.
- Good knowledge of predictive analytics and unsupervised learning
- Good knowledge of Python or similar
- Knowledge in data visualization is of advantage
- Willingness to crack problems and to show self-initiative
- Interest in games
- Knowledge of English language (source code comments/documentation and final report should be in English)