How to Predict Player Churn, with Some Help From ChatGPT

In the world of gaming, companies strive not only to attract players but also to retain them for as long as possible, especially in free-to-play games that rely on in-game micro-transactions. These micro-transactions often involve the purchase of in-game currency, allowing players to acquire items for progression or customization, and funding the game’s development. Monitoring the churn rate, which represents the number of players who stop playing, is crucial. This is because a high churn rate means a significant loss in income, which in turn leads to higher stress levels for developers and managers. This article explores the use of a real-world dataset based on data acquired from a mobile app, specifically focusing on the levels played by users. Leveraging machine learning, which has become an essential part of the technology landscape and forms the basis of Artificial Intelligence (AI), businesses can extract valuable insights from their data. However, building machine learning models typically demands coding and data science expertise, making it inaccessible for many individuals and smaller companies lacking resources for hiring data scientists or powerful hardware to handle complex algorithms. To address these challenges, low-code and no-code machine learning platforms have emerged with the aim of simplifying machine learning and data science processes, thereby mitigating the need for extensive coding knowledge. Examples of such platforms include Einblick, KNIME, Dataiku, Alteryx, and Akkio. <a href="https://towardsdatascience.com/player-churn-rate-prediction-data-analysis-and-visualisation-part-1-12a9fdff9c10">Website</a>