Customer Profiling and Retention Using Recommendation System and Factor Identification to Predict Customer Churn in Telecom Industry

Karimi, Nooria; Dash, Adyasha; Rautaray, Siddharth Swarup; Pandey, Manjusha

doi:10.1007/978-981-33-6518-6_9

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…Lost consumers result in opportunity costs [14]- [16] as a result of lower revenues. As a result, a little increase in client retention might result in a substantial gain in profit [17]- [19]. To identify customers who are going to churn and their motivations, reliable and understandable churn prediction models are required [20], [21].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Classifier Model for Customer Churn Prediction

M A,

K K

2022

International Journal of Electronics and Telecommunications

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Customer churn prediction is used to retain customers at the highest risk of churn by proactively engaging with them. Many machine learning-based data mining approaches have been previously used to predict client churn. Although, single model classifiers increase the scattering of prediction with a low model performance which degrades reliability of the model. Hence, Bag of learners based Classification is used in which learners with high performance are selected to estimate wrongly and correctly classified instances thereby increasing the robustness of model performance. Furthermore, loss of interpretability in the model during prediction leads to insufficient prediction accuracy. Hence, an Associative classifier with Apriori Algorithm is introduced as a booster that integrates classification and association rule mining to build a strong classification model in which frequent items are obtained using Apriori Algorithm. Also, accurate prediction is provided by testing wrongly classified instances from the bagging phase using generated rules in an associative classifier. The proposed models are then simulated in Python platform and the results achieved high accuracy, ROC score, precision, specificity, F-measure, and recall.

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Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Classifier Model for Customer Churn Prediction

M A,

K K

2022

International Journal of Electronics and Telecommunications

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Customer churn prediction model based on hybrid neural networks

Liu,

Xia,

Zhang

et al. 2024

Sci Rep

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

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Churn Prediction in Telecommunications Industry Based on Conditional Wasserstein GAN

Wei

Xie

2022

2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)

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Customer Profiling and Retention Using Recommendation System and Factor Identification to Predict Customer Churn in Telecom Industry

Cited by 5 publications

References 17 publications

An Efficient Hybrid Classifier Model for Customer Churn Prediction

An Efficient Hybrid Classifier Model for Customer Churn Prediction

Customer churn prediction model based on hybrid neural networks

Churn Prediction in Telecommunications Industry Based on Conditional Wasserstein GAN

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