Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (22)
  • Open Access

    ARTICLE

    Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

    Fang Yu1, Wenbin Bi2, Ning Cao3,4,*, 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 >

  • Open Access

    ARTICLE

    Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

    Nagaraju Jajam, Nagendra Panini Challa*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 977-997, 2023, DOI:10.32604/cmc.2023.036098

    Abstract In the insurance sector, a massive volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. The success of retention initiatives is determined not only by the accuracy of forecasting churners but also by the timing of the forecast. Previous works on churn forecast presented models for anticipating churn quarterly or monthly with an emphasis on customers’ static behavior. This paper’s objective is to calculate daily churn based on dynamic variations in client behavior. Training excellent models to… More >

  • Open Access

    ARTICLE

    The Effects of Job Insecurity, Emotional Exhaustion, and Met Expectations on Hotel Employees’ Pro-Environmental Behaviors: Test of a Serial Mediation Model

    Osman M. Karatepe1,*, Raheleh Hassannia1, Tuna Karatepe1, Constanţa Enea2, Hamed Rezapouraghdam1

    International Journal of Mental Health Promotion, Vol.25, No.2, pp. 287-307, 2023, DOI:10.32604/ijmhp.2022.025706

    Abstract There are a plethora of empirical pieces about employees’ pro-environmental behaviors. However, the extant literature has either ignored or not fully examined various factors (e.g., negative or positive non-green workplace factors) that might affect employees’ pro-environmental behaviors. Realizing these voids, the present paper proposes and tests a serial mediation model that examines the interrelationships of job insecurity, emotional exhaustion, met expectations, and proactive pro-environmental behavior. We used data gathered from hotel customer-contact employees with a time lag of one week and their direct supervisors in China. After presenting support for the psychometric properties of the measures via confirmatory analysis in… More >

  • Open Access

    ARTICLE

    Research on Early Warning of Customer Churn Based on Random Forest

    Zizhen Qin, Yuxin Liu, Tianze Zhang*

    Journal on Artificial Intelligence, Vol.4, No.3, pp. 143-154, 2022, DOI:10.32604/jai.2022.031843

    Abstract With the rapid development of interest rate market and big data, the banking industry has shown the obvious phenomenon of “two or eight law”, 20% of the high quality customers occupy most of the bank’s assets, how to prevent the loss of bank credit card customers has become a growing concern for banks. Therefore, it is particularly important to establish a customer churn early warning model. In this paper, we will use the random forest method to establish a customer churn early warning model, focusing on the churn of bank credit card customers and predicting the possibility of future churn… More >

  • Open Access

    ARTICLE

    Social Opinion Network Analytics in Community Based Customer Churn Prediction

    Ayodeji O. J Ibitoye1,*, 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 >

  • Open Access

    ARTICLE

    A Big Data Based Dynamic Weight Approach for RFM Segmentation

    Lin Lang1, Shuang Zhou1, Minjuan Zhong1,*, Guang Sun1, Bin Pan1, Peng Guo1,2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3503-3513, 2023, DOI:10.32604/cmc.2023.023596

    Abstract Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data… More >

  • Open Access

    ARTICLE

    Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

    A. S. Harish*, C. Malathy

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 589-600, 2023, DOI:10.32604/iasc.2023.032030

    Abstract Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers. It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities. The volume and volatility of the business makes it one of the prospective fields for analytical study and data modeling. This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting, customer targeting, customized offers, value proposition etc. The segmentation could be on various… More >

  • Open Access

    ARTICLE

    Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

    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 >

  • Open Access

    ARTICLE

    Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

    K. Selvasheela1,*, A. M. Abirami2, Abdul Khader Askarunisa3

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2537-2552, 2023, DOI:10.32604/csse.2023.029148

    Abstract Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review.… More >

  • Open Access

    ARTICLE

    Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

    Olfat M. Mirza1, G. Jose Moses2, R. Rajender3, E. Laxmi Lydia4, Seifedine Kadry5, Cheadchai Me-Ead6, Orawit Thinnukool7,*

    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 >

Displaying 1-10 on page 1 of 22. Per Page  

Share Link