Customer Churn Prediction Using Machine Learning Approaches

Srinivasan, R.; Rajeswari, D; Elangovan, G.

doi:10.1109/iceconf57129.2023.10083813

Cited by 10 publications

(1 citation statement)

References 17 publications

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“…In the same year Zamil et al [6] used LR again to detect the probability of customer churn and reached accuracy of 80%. Srinivasan et al [7] used the popular machine learning algorithms like LR and RF to accurately identify the customers who are most likely to churn. The author worked on real customer records obtained from a telecommunication company.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Hussien,

Mahgoub,

Youssef

et al. 2024

IJEECS

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<p><span>Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes place). Furthermore, an extensive data exploratory analysis is performed to gain more insights from the data to identify the important features. Transactional data and static profile data are integrated to feed a deep learning model which is implemented using two methodologies: time-series approach and statistical approach. It is found that the time-series approach gives the best performance and fulfills all the requirements. The experiments show that the proposed framework introduces a good overall performance in comparison to existing approaches based on standard metrics like accuracy and mean absolute error (MAE) values. What makes the proposed work novel and special is that it is the first approach that addresses the telecom customer’s next future interaction not just churn prediction like the other approaches in literature.</span></p>

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Section: Literature Reviewmentioning

confidence: 99%

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Hussien,

Mahgoub,

Youssef

et al. 2024

IJEECS

View full text Add to dashboard Cite

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Covid 19 diagnosis systems based on convolutional neural network techniques

Faizan,

Babu,

Srinivasan

2024

AIP Conference Proceedings

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ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry

Saha,

Haque

et al. 2024

IEEE Access

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In the Telecommunication Industry (TCI) customer churn is a significant issue because the revenue of the service provider is highly dependent on the retention of existing customers. In this competitive market, it is essential for the service providers to figure out the concerns of their existing customers regarding their services as the cancellation of the services by the customers and switching to new service providers will not bring any good to the service provider. In the context of TCI, numerous research have been made to predict customer churn though, after the performance evaluation of these studies, it shows that there is enough room for progress. Therefore, in this study, we proposed a novel customer churn prediction architecture namely ChurnNet to predict customer churn in TCI. In our proposed ChurnNet, the 1D convolution layer is integrated with residual block, squeeze and excitation block, and spatial attention module to improve the performance. Residual block aids in solving the vanishing gradient problem. Squeeze and excitation block and spatial attention module enable the ChurnNet to understand the interdependency between and within the channels respectively. To evaluate the performance, the experiment is performed on 3 publicly available datasets. As the datasets have significant class imbalance issues, three data balancing techniques such as SMOTE, SMOTEEN, and SMOTETomek are performed. Along with 10-fold crossvalidation and after going through the rigorous experiment it was found that ChurnNet performed better than the state-of-the-art and obtained 95.59%, 96.94%, and 97.52% accuracy on 3 benchmark datasets respectively.INDEX TERMS Squeeze and excitation, spatial attention, residual block, churn prediction, ChurnNet.

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Customer Churn Prediction Using Machine Learning Approaches

Cited by 10 publications

References 17 publications

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

Covid 19 diagnosis systems based on convolutional neural network techniques

ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry

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