Results 191 to 200 of about 899,252 (240)

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   +1 more source

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   +1 more source

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

Customer Churn Prediction in the Telecom Sector

2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)
Customer churn, the phenomenon of customers terminating their subscription or services with a telecom provider, poses a significant challenge in the telecom industry.
Pallavi Aggarwal, Vaidehi Vijayakumar
semanticscholar   +1 more source

Enhancing Customer Churn Prediction in Telecommunications: An Adaptive Ensemble Learning Approach

arXiv.org
Customer churn, the discontinuation of services by existing customers, poses a significant challenge to the telecommunications industry. This paper proposes a novel adaptive ensemble learning framework for highly accurate customer churn prediction.
Mohammed Affan Shaikhsurab, Pramod Magadum   +1 more
semanticscholar   +1 more source

Personalized and Contextualized Data Analysis for E-Commerce Customer Retention Improvement With Bi-LSTM Churn Prediction

IEEE transactions on consumer electronics
This study examines the issue of customer churn in the context of e-commerce businesses. Customer churn, or losing customers, poses a significant challenge for these businesses as they strive to retain a loyal customer base.
Qing Wei
semanticscholar   +1 more source

Predicting Customer Churn at QWE Inc.

Darden Business Publishing Cases, 2017
This case exposes students to predictive analytics as applied to discrete events with logistic regression. The VP of customer services for a successful start-up wants to proactively identify customers most likely to cancel services or “churn.” He assigns the task to one of his associates and provides him with data on customer behavior and his intuition
openaire   +1 more source

Customer-Churn Research Based on Customer Segmentation

2009 International Conference on Electronic Commerce and Business Intelligence, 2009
This article explores the unique features of the customer relationship management (CRM) system in Telecom industry and presents a customer-churn model based on customer segmentation. First, the improved Fuzzy C-means clustering algorithm is used to segment customer and conclude high value customer group characteristics.
Xiaobin Zhang, Gao Feng, Huang Hui
openaire   +1 more source

Predicting Customer Churn in Telecommunication Industry Using Convolutional Neural Network Model

Social Science Research Network
: In this study a Convolutional Neural Network (CNN) model was proposed for the prediction of customer churn in a telecommunication industry. Many supervised machine learning models have been built and used for predicting customer churn in past ...
Adebola K. Ojo
semanticscholar   +1 more source

The Impact of SMOTE and ADASYN on Random Forest and Advanced Gradient Boosting Techniques in Telecom Customer Churn Prediction

2024 10th International Conference on Web Research (ICWR)
This paper explores the capability of various machine learning algorithms, including Random Forest and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector.
Mehdi Imani, Zahra Ghaderpour, M. Joudaki, Ali Beikmohammadi   +3 more
semanticscholar   +1 more source