Business Closure Prediction Based on Users Rating and Review Using H2O

Predicting business closure refers to the process of analyzing various factors and making predictions about the likelihood that a business will close. Closing a business does not only affect business owner, but it also has an impact on employees. There have been many studies conducted on predicting business closures based on managerial or economic factors, but research discussing predicting business closures based on customer reviews or ratings is still limited. Prediction of business closures is made to assist business owner in reducing the risk of loss and business closure. In this research, business closure predictions are made based on the combination between customer ratings and review data. Merging these two data is done to see whether it can increase the level of accuracy of the predicted results of business closures compared to rating or review data alone. Predictions are made using a deep learning framework, H20, which can prevent the model from experiencing overfitting.

Authors

Publication Details

Published
2024-09-12
DOI
https://doi.org/10.1109/ictiia61827.2024.10761845
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
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article

Business Closure Prediction Based on Users Rating and Review Using H2O

Muhammad Fermi Pasha, Ronsen Purba, Enjelin
Customer churn and segmentation
article

Business Closure Prediction Based on Users Rating and Review Using H2O

Muhammad Fermi Pasha, Ronsen Purba, Enjelin
article en

Abstract

Predicting business closure refers to the process of analyzing various factors and making predictions about the likelihood that a business will close. Closing a business does not only affect business owner, but it also has an impact on employees. There have been many studies conducted on predicting business closures based on managerial or economic factors, but research discussing predicting business closures based on customer reviews or ratings is still limited. Prediction of business closures is made to assist business owner in reducing the risk of loss and business closure. In this research, business closure predictions are made based on the combination between customer ratings and review data. Merging these two data is done to see whether it can increase the level of accuracy of the predicted results of business closures compared to rating or review data alone. Predictions are made using a deep learning framework, H20, which can prevent the model from experiencing overfitting.

Openalex Percentile: Top 69%
Customer churn and segmentation
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Business Closure Prediction Based on Users Rating and Review Using H2O — Muhammad Fermi Pasha, Ronsen Purba, et al. · (2024) | TGRS Research Map | TGRS