Results 201 to 210 of about 1,523,124 (234)

Call Churn Prediction with PySpark

2019
Different markets over the world are ending up progressively more saturated, with an ever-increasing number of customers swapping their enrolled benefits between contending organizations. Consequently, organizations have understood that they should center their promoting endeavors in client maintenance instead of client procurement.
Mark Sheridan Nonghuloo, Rangapuram Aravind Reddy, Ganji Manideep, M. R. SarathVamsi, Kamppa Lavanya   +4 more
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ADTreesLogit model for customer churn prediction

Annals of Operations Research, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhou, Y, Li, L, Qi, J, Zhang, L, Liu, Y, Liang, L, Li, H, Shen, Y   +7 more
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Predict Churning Customers – An Explorative Study

2022 17th Iberian Conference on Information Systems and Technologies (CISTI), 2022
Some banks and business managers are facing the problem of customer credit card attrition. Therefore, it was necessary to identify new strategies for banks and business managers to keep their customers satisfied. In this paper, we analyze the data from a fictitious data source available on Kaggle, to find out the reason behind this and to predict ...
Ferreira, Tomás, Pita, Pedro, Brito, Isabel   +2 more
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Adoption of Churn Recognition System to Predict Customer Churn

2023
In the cutthroat competitive arena, it is a very challenging task for any enterprise to make a balance between retaining its existing loyal customers and attracting new customers. It is a tedious task to find the right segment of active customers and understand the reason behind churn numbers. It is said that it is five times more costly to attract new
Yuvraj Sharma, Rajender Gurram, Naveen Saharan   +2 more
openaire   +1 more source

Churn Prediction using Ensemble Learning

Proceedings of the 4th International Conference on Machine Learning and Soft Computing, 2020
With a wealth of information on hand from the Internet, customers now can easily identify and switch to alternatives. In addition to this, a consensus has been reached that the cost of securing new customers is substantially higher than the cost of retaining the current customers.
Xing Wang, Khang Nguyen, Binh P. Nguyen
openaire   +2 more sources

Customer Churn Prediction for Telecom Services

2012 IEEE 36th Annual Computer Software and Applications Conference, 2012
Customer churn is a big concern for telecom service providers due to its associated costs. This short paper briefly explains our ongoing work on customer churn prediction for telecom services. We are working on data mining methods to accurately predict customers who will change and turn to another provider for the same or similar service.
Utku Yabas, Hakki C. Cankaya, Turker Ince   +2 more
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Deep Neural Pipeline for Churn Prediction

2018 17th RoEduNet Conference: Networking in Education and Research (RoEduNet), 2018
Customer churn is an essential retail metric used in business predictive analytics systems to quantify the number of customers who left a company. All retail and business to consumer companies carefully analyze customer behavior to prevent them to cease their relationship with the company, in other words to make churn.
Andrei Simion-Constantinescu, Andrei Ionut Damian, Nicolae Tapus, Laurentiu-Gheorghe Piciu, Alexandru Purdila, Bogdan Dumitrescu   +5 more
openaire   +2 more sources

TreeLogit Model for Customer Churn Prediction

2006 IEEE Asia-Pacific Conference on Services Computing (APSCC'06), 2006
For the purpose of improving the predictive accuracy and interpret ability of churn prediction model, TreeLogit model, which integrates the advantage of AD tree model and logistic regression model, is proposed in this paper to predict customers' churn propensities.
Jiayin Qi, Yangming Zhang, Yingying Zhang, Shuang Shi   +3 more
openaire   +2 more sources

Predicting Customer Churn for Insurance Data

2020
Most organisations employ customer relationship management systems to provide a strategic advantage over their competitors. One aspect of this is applying a customer lifetime value to each client which effectively forms a fine-grained ranking of every customer in their database. This is used to focus marketing and sales budgets and, in turn, generate a
Michael Scriney, Dongyun Nie, Mark Roantree   +2 more
openaire   +2 more sources

Customer Churn Prediction by Hybrid Model

2006
In order to improve the performance of a data mining model, many researchers have employed a hybrid model approach in solving a problem. There are two types of approach to build a hybrid model, i.e., the whole data approach and the segmented data approach.
Jae Sik Lee, Jin Chun Lee
openaire   +1 more source