Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games

Kristensen, Jeppe Theiss; Burelli, Paolo

doi:10.1109/cig.2019.8848106

Cited by 15 publications

(8 citation statements)

References 22 publications

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“…Bonometti et al [6] compared aggregated and temporal data, and find that models with temporal data outperform the other kind. Kristensen et al [20] propose using stacked LSTM networks with a combination of aggregated and time-series data as their inputs.…”

Section: Related Work 21 Churn Predictionmentioning

confidence: 99%

Predicting Game Difficulty and Churn Without Players

Roohi

Relas²,

Takatalo³

et al. 2020

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

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Figure 1: Scatter plots depicting the relation of pass rate (a measure of level difficulty) and churn rate over 168 game levels of Angry Birds Dream Blast, in both real player data and our simulations. Here, churn is defined as not playing for 7 days. The colors denote level numbers. The baseline simulation model predicts pass rate and churn directly from AI gameplay. Our proposed extended model augments this with a simulation of how the player population evolves over the levels.

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Section: Related Work 21 Churn Predictionmentioning

confidence: 99%

Predicting Game Difficulty and Churn Without Players

Roohi

Relas²,

Takatalo³

et al. 2020

Proceedings of the Annual Symposium on Computer-Human Interaction in Play

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“…Researchers also conducted churn studies in online gaming industry. CNN and LSTM models [24], LSTM and LSTM-based models [25], and LSTM and other machine learning models [26] were used in these churn studies, which analyze the players' in-game purchases, game logs, and membership status. Accuracy, AUC, and F1-score were used as evaluation metrics for these models.…”

Section: Literature Reviewmentioning

confidence: 99%

Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Seymen

Ölmez

Doğan

et al. 2023

Gazi University Journal of Science

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Churn studies have been used for many years to increase profitability as well as to make customer-company relations sustainable. Ordinary artificial neural network (ANN) and convolution neural network (CNN) are widely used in churn analysis due to their ability to process large amounts of customer data. In this study, an ANN and a CNN model are proposed to predict whether customers in the retail industry will churn in the future. The models we propose were compared with many machine learning methods that are frequently used in churn prediction studies. The results of the models were compared via accuracy classification tools, which are precision, recall, and AUC. The results showed that the CNN model produced a 97.62% of accuracy rate which resulted in a better classification and prediction success than other compared models

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“…This is a large dataset that stores information about tens of thousands of mobile games and hundreds of millions of user-application interactions. In the study (Kristensen & Burelli, 2019), authors present several LSTM based neural network architectures that use both sequential and historical data to predict loyalty prediction by analyzing user behaviour. The dataset contains player logs of a mobile game.…”

Section: Related Workmentioning

confidence: 99%

Prediction of user loyalty in mobile applications using deep contextualized word representations

Kilimci

2021

Journal of Information and Telecommunication

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Customer loyalty is important for many industries, including banking, telecommunications, gaming, and shopping, in terms of sustainability. In mobile applications, it is observed that the demand rises with the usage of mobile devices such as smartphones. Therefore, it is important to predict when players tend to leave an application. Most of the studies so far focus on churn prediction or customer loyalty in mobile applications by analyzing demographic, economic, and behavioral data about customers. In this work, we introduce sentiment analysis-based customer loyalty prediction in mobile applications using word embeddings, deep learning algorithms, and deep contextualized word representations. To our knowledge, this is the first study to evaluate loyalty of customers analyzing sentiments of users from their comments using deep learning, word embedding, and deep contextualized word representation models. For this purpose, CNNs, RNNs, LSTMs, BERT, MBERT, DistilBERT, RoBERT are used for classification purpose. On the other hand, word embedding models such as Word2Vec, GloVe, and FastText are employed for text representation. To demonstrate the impact of proposed model, comprehensive experiments are performed on seven different datasets. The experiment results show sentiment analysis of users in mobile applications can be a powerful indicator in terms of predicting customer loyalty.

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Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games

Cited by 15 publications

References 22 publications

Predicting Game Difficulty and Churn Without Players

Predicting Game Difficulty and Churn Without Players

Customer Churn Prediction Using Ordinary Artificial Neural Network and Convolutional Neural Network Algorithms: A Comparative Performance Assessment

Prediction of user loyalty in mobile applications using deep contextualized word representations

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