This research takes a deep dive into player retention and monetization strategies within the mobile gaming industry. With the mobile gaming industry expanding rapidly and becoming more competitive, game developers must understand how to keep players engaged. We used data science to investigate how players behave, what keeps them returning, and how games may generate more revenue. By studying how people play games and what they think, we want to identify evident actions that game developers can take to make their games more enjoyable and lucrative.
Obtain dataset related to the research of player retention and monetization strategies in mobile games. Goal is to clean, structure, and preprocess the data to ensure consistency and reliability for analysis, providing the groundwork for understanding player behaviors and game monetization strategies.
Through exploratory data analysis uncover the trends and connections in the dataset, specially focusing on the impact of gaming elements, user engagement, and user retention and expenditure.
Find a predictive model that will be chosen based on its ability to properly forecast user engagement and monetization efforts.
Metrics like R-squared values, MSE (Mean Squared Error), and MAE (Mean Absolute Error) can be used to evaluate the efficacy of the trained model. Utilized cross-validation method to confirm the model’s prediction, ensuring its applicability across different datasets and robustness to overfitting.