Zobrazeno 1 - 10
of 325
pro vyhledávání: '"Customer churn prediction"'
Publikováno v:
AIMS Mathematics, Vol 9, Iss 2, Pp 2781-2807 (2024)
In today's competitive market, predicting clients' behavior is crucial for businesses to meet their needs and prevent them from being attracted by competitors. This is especially important in industries like telecommunications, where the cost of acqu
Externí odkaz:
https://doaj.org/article/8d602adc050b44c488b628bf37295a5a
Autor:
João B. G. Brito, Guilherme B. Bucco, Rodrigo Heldt, João L. Becker, Cleo S. Silveira, Fernando B. Luce, Michel J. Anzanello
Publikováno v:
Financial Innovation, Vol 10, Iss 1, Pp 1-29 (2024)
Abstract Managing customer retention is critical to a company’s profitability and firm value. However, predicting customer churn is challenging. The extant research on the topic mainly focuses on the type of model developed to predict churn, devoti
Externí odkaz:
https://doaj.org/article/846d239c10db46cabe3d26b1214e0a03
Publikováno v:
IEEE Open Journal of the Computer Society, Vol 5, Pp 27-38 (2024)
This study proposes a hybrid approach to predict customer churn by combining statistic approaches and machine learning models. Unlike traditional methods, where churn is defined by a fixed period of time, the proposed algorithm uses the probability o
Externí odkaz:
https://doaj.org/article/ce41d65d51904749ad97013d2bd7bda1
Autor:
Fatima E. Usman-Hamza, Abdullateef O. Balogun, Salahdeen K. Nasiru, Luiz Fernando Capretz, Hammed A. Mojeed, Shakirat A. Salihu, Abimbola G. Akintola, Modinat A. Mabayoje, Joseph B. Awotunde
Publikováno v:
Scientific African, Vol 23, Iss , Pp e02054- (2024)
Customer churn is a vital and reoccurring problem facing most business industries, particularly the telecommunications industry. Considering the fierce competition among telecommunications firms and the high expenses of attracting and gaining new sub
Externí odkaz:
https://doaj.org/article/bd2c5041afc54e0492b132b0aba5fe09
Autor:
Sharmila K. Wagh, Aishwarya A. Andhale, Kishor S. Wagh, Jayshree R. Pansare, Sarita P. Ambadekar, S.H. Gawande
Publikováno v:
Results in Control and Optimization, Vol 14, Iss , Pp 100342- (2024)
In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from o
Externí odkaz:
https://doaj.org/article/8c567f19a8fe4b52aed9747c9c4e1d96
Publikováno v:
Journal of Engineering Technology and Applied Physics, Vol 5, Iss 2, Pp 99-107 (2023)
In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors t
Externí odkaz:
https://doaj.org/article/00288d0301fa460b8a647cd3d9c5fb1e
Autor:
Youngjung Suh
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-35 (2023)
Abstract Customer churn is a major issue for large enterprises. In particular, in the rental business sector, companies are looking for ways to retain their customers because they are their main source of revenue. The main contribution of our work is
Externí odkaz:
https://doaj.org/article/895997b5822b4cb2bf5297e76f4f0747
Autor:
Anitha M A, Sherly K K
Publikováno v:
International Journal of Electronics and Telecommunications, Vol vol. 69, Iss No 1, Pp 11-18 (2023)
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 classif
Externí odkaz:
https://doaj.org/article/b24a72a72b254254b082a0dc51c6d4c6
Autor:
Victor Chang, Karl Hall, Qianwen Ariel Xu, Folakemi Ololade Amao, Meghana Ashok Ganatra, Vladlena Benson
Publikováno v:
Algorithms, Vol 17, Iss 6, p 231 (2024)
Customer churn is a significant concern, and the telecommunications industry has the largest annual churn rate of any major industry at over 30%. This study examines the use of ensemble learning models to analyze and forecast customer churn in the te
Externí odkaz:
https://doaj.org/article/f4f2391f016345be90f94456ed466c04
Autor:
Daeho Seo, Yongmin Yoo
Publikováno v:
IEEE Access, Vol 11, Pp 7924-7932 (2023)
With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important ar
Externí odkaz:
https://doaj.org/article/2bef4d95ee004369a89cd1a5ff5e6ae0