In this paper, we investigate the problem of churn prediction in Massively multiplayer online role-playing games (MMORPGs) from a social science perspective and develop models incorporating theories of player motivation. The ability to predict player churn can be a valuable resource to game developers designing customer retention strategies. The results from our theory-driven model significantly outperform a diffusion-based churn prediction model on the same dataset. We describe the synthesis between a theory-driven approach and a data-driven approach to a problem and examine the trade-offs involved between the two approaches in terms of prediction accuracy, interpretability and model complexity. We observe that even though the theory-driven model is not as accurate as the data-driven one, the theory-driven model itself can be more interpretable to the domain experts and hence, more preferable over a complex data-driven model. We perform lift analysis of the two models and find that if a marketing effort is restricted in the number of customers it can contact, the theory-driven model would offer much better return-on-investment by identifying more customers among that restricted set who have the highest probability of churn. Finally, we use a clustering technique to partition the dataset and then build an ensemble on the partitioned dataset for better performance. Experiment results show that the ensemble performs notably better than the single classifier in terms of its recall value, which is a highly desirable property in the churn prediction problem.