Fang Yu¹, Wenbin Bi², Ning Cao^3,4,*, Hongjun Li¹, Russell Higgs⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1-17, 2023, DOI:10.32604/csse.2023.018349

Abstract In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation, at the smart contract level of the blockchain, a customer churn prediction framework based on situational awareness and integrating customer attributes, the impact of project hotspots on customer interests, and customer satisfaction with the project has been built. This framework introduces the background factors in the financial customer environment, and further discusses the relationship between customers, the background of customers and the characteristics of pre-lost customers. The improved Singular Value Decomposition (SVD) algorithm and… More >

Ayodeji O. J Ibitoye^1,*, Olufade F. W Onifade²

Journal on Big Data, Vol.4, No.2, pp. 87-95, 2022, DOI:10.32604/jbd.2022.024533

Abstract Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern… More >

Vani Haridasan^*, Kavitha Muthukumaran, K. Hariharanath

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3531-3544, 2023, DOI:10.32604/iasc.2023.030628

Abstract Customer retention is one of the challenging issues in different business sectors, and various firms utilize customer churn prediction (CCP) process to retain existing customers. Because of the direct impact on the company revenues, particularly in the telecommunication sector, firms are needed to design effective CCP models. The recent advances in machine learning (ML) and deep learning (DL) models enable researchers to introduce accurate CCP models in the telecommunication sector. CCP can be considered as a classification problem, which aims to classify the customer into churners and non-churners. With this motivation, this article focuses on designing an arithmetic optimization algorithm… More >

Olfat M. Mirza¹, G. Jose Moses², R. Rajender³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Cheadchai Me-Ead⁶, Orawit Thinnukool^7,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3757-3769, 2022, DOI:10.32604/cmc.2022.030428

Abstract Presently, customer retention is essential for reducing customer churn in telecommunication industry. Customer churn prediction (CCP) is important to predict the possibility of customer retention in the quality of services. Since risks of customer churn also get essential, the rise of machine learning (ML) models can be employed to investigate the characteristics of customer behavior. Besides, deep learning (DL) models help in prediction of the customer behavior based characteristic data. Since the DL models necessitate hyperparameter modelling and effort, the process is difficult for research communities and business people. In this view, this study designs an optimal deep canonically correlated… More >

Soban Arshad¹, Khalid Iqbal^1,*, Sheneela Naz², Sadaf Yasmin¹, Zobia Rehman²

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4283-4301, 2022, DOI:10.32604/cmc.2022.025442

Abstract Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the… More >