In this paper, we present churn prediction techniques that have been released so far. Churn prediction is used in the fields of Internet services, games, insurance, and management. However, since it has been used intensively to increase the predictability of various industry/academic fields, there is a big difference in its definition and utilization. In this paper, we collected the definitions of churn used in the fields of business administration, marketing, IT, telecommunications, newspapers, insurance and psychology, and described their differences. Based on this, we classified and explained churn loss, feature engineering, and prediction models. Our study can be used to select the definition of churn and its associated models suitable for the service field that researchers are most interested in by integrating fragmented churn studies in industry/academic fields.

Deep embedded clustering (DEC) is a representative clustering algorithm that leverages deep-learning frameworks. DEC jointly learns low-dimensional feature representations and optimizes the clustering goals but only works with numerical data. However, in practice, the real-world data to be clustered includes not only numerical features but also categorical features that DEC cannot handle. In addition, if the difference between the soft assignment and target values is large, DEC applications may suffer from convergence problems. In this study, to overcome these limitations, we propose a deep embedded clustering framework that can utilize mixed data to increase the convergence stability using soft-target updates; a concept that is borrowed from an improved deep Q learning algorithm used in reinforcement learning. To evaluate the performance of the framework, we utilized various benchmark datasets composed of mixed data and empirically demonstrated that our approach outperformed existing clustering algorithms in most standard metrics. To the best of our knowledge, we state that our work achieved state-of-the-art performance among its contemporaries in this field.

Game experience testing, an essential process for developing and servicing high-quality games, aims to evaluate and improve the play experience from the user perspective. Previous game experience tests are usually held offline with recruited test participants. Participants play the game in a controlled environment, and their experiences are recorded and analyzed by domain experts. However, this process not only requires substantial resources in terms of time and cost but also misses momentary data that can infer the user’s experience directly and quantitatively, such as facial expressions or body gestures of the participants while playing the game. In this paper, we propose a framework that can automatically collect and analyze video data of participants in a remote environment. We designed various experiments to verify that the proposed framework can automatically collect and analyze facial data in a remote environment. The experimental results show that our framework can be applied in gaming experience tests for popular commercial games currently in service. This paper presents a pioneering work that applies the face analysis method to the commercial game development process, and its purpose is beyond that of a pure research work.