A Comprehensive Review of LSTM-Based Churn Prediction Models in the Gaming Industry

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Vatani Fatemeh; Dorrigiv Morteza; Emami Samaneh

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Modeling & Simulation in Electrical & Electronics Engineering Year:2023 | Volume:3 | Issue:3 Page(s): 51-60

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

A Comprehensive Review of LSTM-Based Churn Prediction Models in the Gaming Industry

Author(s)

Vatani Fatemeh | Dorrigiv Morteza | Emami Samaneh | Issue Writer Certificate

Keywords

Customer churn

Classification

Deep Neural Network

Long Short-term Memory

Survival analysis

Game data mining

Abstract

Client churn is a significant issue affecting companies across various industries. In the gaming sector, customer loss is particularly critical as it directly impacts revenue, profit margins, and customer retention. Inaccurate predictions of client churn can lead to substantial revenue losses. Churn prediction involves identifying customers who are most likely to cancel their subscriptions. This practice has become essential for many modern organizations due to its performance benefits, aiding businesses in calculating revenue growth and client retention metrics. This paper classifies player churn prediction models into seven main categories to comprehensively review the existing literature. This Classification enhances the understanding of various methodologies used in the field and highlights potential areas for future research. Notably, Long Short-term Memory (LSTM) networks, a type of recurrent neural network, have demonstrated significant potential among deep learning models. This paper examines the contribution of LSTM networks in predicting churn in computer games.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans