Customer churn prediction model based on hybrid neural networks DOI Creative Commons
Xinyu Liu, Guoen Xia, Xianquan Zhang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Abstract In today’s competitive market environment, accurately identifying potential churn customers and taking effective retention measures are crucial for improving customer ensuring the sustainable development of an organization. However, traditional machine learning algorithms single deep models have limitations in extracting complex nonlinear time-series features, resulting unsatisfactory prediction results. To address this problem, study proposes a hybrid neural network-based model, CCP-Net. data preprocessing stage, ADASYN sampling algorithm balances sample sizes churned non-churned to eliminate negative impact imbalance on model performance. feature extraction CCP-Net uses Multi-Head Self-Attention learn global dependencies input sequences, combines with BiLSTM capture long-term sequential data, CNN extract local ultimately generates Experimental results cross-validation Telecom, Bank, Insurance, News datasets show that outperforms comparison all performance metrics. For example, achieves Precision 92.19% Telecom dataset, 91.96% Bank 95.87% Insurance 95.12% which compares other network models, improvement ranges from 1% 3%. These indicate design effectively improves accuracy robustness prediction, enabling it be widely applied different industries, especially financial, telecommunication, media fields, provide more comprehensive management strategies enterprises.

Language: Английский

Advancements in Natural Language Processing: Implications, Challenges, and Future Directions DOI Creative Commons
Supriyono Supriyono, Aji Prasetya Wibawa,

Suyono

et al.

Telematics and Informatics Reports, Journal Year: 2024, Volume and Issue: 16, P. 100173 - 100173

Published: Nov. 7, 2024

Language: Английский

Citations

13
Efficient shrinkage temporal convolutional network model for photovoltaic power prediction DOI
Min Wang, Congjun Rao, Xinping Xiao

et al.

Energy, Journal Year: 2024, Volume and Issue: 297, P. 131295 - 131295

Published: April 15, 2024

Language: Английский

Citations

8
Application of GWO-attention-ConvLSTM Model in Customer Churn Prediction and Satisfaction Analysis in Customer Relationship Management DOI Creative Commons
Hui Zhang, Weihua Zhang

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e37229 - e37229

Published: Sept. 1, 2024

Customer Relationship Management (CRM) is vital in modern business, aiding the management and analysis of customer interactions. However, existing methods struggle to capture dynamic complex nature relationships, as traditional approaches fail leverage time series data effectively. To address this, we propose a novel GWO-attention-ConvLSTM model, which offers more effective prediction churn satisfaction. This model utilizes an attention mechanism focus on key information integrates ConvLSTM layer spatiotemporal features, effectively modeling temporal patterns data. We validate our proposed multiple real-world datasets, including BigML Telco Churn dataset, IBM Cell2Cell Orange Telecom dataset. Experimental results demonstrate significant performance improvements compared baseline models across these datasets. For instance, achieves accuracy 95.17%, recall 93.66%, F1 score 92.89%, AUC 95.00%. Similar are validated other In conclusion, makes advancements CRM domain, providing powerful tools for predicting analyzing By addressing limitations leveraging capabilities deep learning, mechanisms, optimization algorithms, paves way improving relationship practices driving business success.

Language: Английский

Citations

2
Q-ensemble learning for customer churn prediction with blockchain-enabled data transparency DOI
Usama Arshad, Gohar F. Khan, Fawaz Khaled Alarfaj

et al.

Annals of Operations Research, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 17, 2024

Language: Английский

Citations

0
A Novel Deep Convolutional Neural Network Algorithm for Equity Price Prediction DOI Open Access

Jesmine Mary Antony,

Natarajan Sundaram

International Research Journal of Multidisciplinary Technovation, Journal Year: 2024, Volume and Issue: unknown, P. 275 - 291

Published: Nov. 30, 2024

Predicting stock prices is one of the difficult issues for researchers and investors. The study suggests an equity price prediction based on feature neural network extraction. We expect using technovative forecasting from traditional Machine Learning (ML) models namely Linear Regression (LR), Autoregressive Integrated Moving Averages (ARIMA), advanced Deep (DL) algorithms such as Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) Convolutional Network-Long (CNN-LSTM). select seven features historical data: date, close, open, high, low, volume, change %. study’s novelty accuracy compared to step-by-step backtesting methodology ML DL algorithms. first use CNN extract data consisting items preceding 10 days 100 days. After that extracted LSTM predict price. Finally, used robotic error measure analysis, MAE, RMSE, R2, assess all four models. CNN-LSTM model provides a consistent forecast measures with maximum exactness ranging 0 1, MAE-0.03, RMSE-0.04, R2-0.98. proposed maintained its efficiency throughout process when LR, ARIMA, LSTM-RNN conducts robustness hypothesis check ANOVA test statistic superior predictability accuracy. In addition, this technique gives academics real-world experience analyzing financial time series confident investment ideas

Language: Английский

Citations

0
Intensified customer churn Prediction: Connectivity with weighted Multi-Layer Perceptron and Enhanced Multipath Back Propagation DOI

S. Arockia Panimalar,

A. S. Krishnakumar,

S. Senthil Kumar

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125993 - 125993

Published: Dec. 1, 2024

Language: Английский

Citations

0
Customer churn prediction model based on hybrid neural networks DOI Creative Commons
Xinyu Liu, Guoen Xia, Xianquan Zhang

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 28, 2024

Abstract In today’s competitive market environment, accurately identifying potential churn customers and taking effective retention measures are crucial for improving customer ensuring the sustainable development of an organization. However, traditional machine learning algorithms single deep models have limitations in extracting complex nonlinear time-series features, resulting unsatisfactory prediction results. To address this problem, study proposes a hybrid neural network-based model, CCP-Net. data preprocessing stage, ADASYN sampling algorithm balances sample sizes churned non-churned to eliminate negative impact imbalance on model performance. feature extraction CCP-Net uses Multi-Head Self-Attention learn global dependencies input sequences, combines with BiLSTM capture long-term sequential data, CNN extract local ultimately generates Experimental results cross-validation Telecom, Bank, Insurance, News datasets show that outperforms comparison all performance metrics. For example, achieves Precision 92.19% Telecom dataset, 91.96% Bank 95.87% Insurance 95.12% which compares other network models, improvement ranges from 1% 3%. These indicate design effectively improves accuracy robustness prediction, enabling it be widely applied different industries, especially financial, telecommunication, media fields, provide more comprehensive management strategies enterprises.

Language: Английский

Citations

0