scholarly journals One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Jin Xiao ◽  
Bing Zhu ◽  
Geer Teng ◽  
Changzheng He ◽  
Dunhu Liu
Keyword(s):  
Customer Value ◽  
Missing Values ◽  
Credit Scoring ◽  
Customer Relationship ◽  
Classifier Ensemble ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
One Step

Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

scholarly journals Prediction of Customer Churn on e-Retailing

2020 ◽  
Vol 8 (6) ◽  
pp. 5541-5545
Keyword(s):  
Customer Relationship ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Interaction ◽  
Huge Impact ◽  
Business To Consumer ◽  
Past Data ◽  
Customer Services ◽  
And Behavior

The technology has always been an instigating factor in progress for human civilization which resulted in driving the customer services to a greater need. The enrichment of technology has amplified and embellished the customer interaction among various business to consumer sectors. These technological upgrading have a huge impact on the retail industry which is an ever-growing market with key competitors around the world. In a consortium of multiple competitors in the same business, the re-engagement of disinterested customers is essential rather than winning a new customer. The sustenance of a customer can be figure out by Churn Prediction. Churn prediction is a new promising method in customer relationship management to analyze customer retention in subscription-based business. It is the activity of identifying customer with a high probability to discontinue the company based on analyzing their past data and behavior. It looks at what kind of customer data are typically used, do some analysis of the features chosen, and initiate a churn prediction model. Thus, churn prediction is a valuable approach in identifying and profiling the customers at risk.

scholarly journals Customer Churn Prediction using Logistic Regression with Regularization and Optimization Technique

2020 ◽  
Vol 9 (9) ◽  
pp. 334-339
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Optimization Technique ◽  
Customer Relationship ◽  
Communication Process ◽  
Prediction Algorithm ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
Customer Churn Prediction

Customer Relationship Management (CRM) is a challenging issue in marketing to better understand the customers and maintaining long-term relationships with them to increase the profitability. It plays a vital role in customer centered marketing domain which provides a better service and satisfies the customer requirements based on their characteristics in consuming patterns and smoothes the relationship where various representatives communicate and collaborate. Customer Churn prediction is one of the area in CRM that explores the transaction and communication process and analyze the customer loyalty. Data mining ease this process with classification techniques to explore pattern from large datasets. It provides a good technical support to analyze large amounts of complex customer data. This research paper applies data mining classification technique to predict churn customers in three variant sectors Banking, Ecommerce and Telecom. For Classification, enhanced logistic regression with regularization and optimization technique is applied. The work is implemented in Rapid miner tool and the performance of the prediction algorithm is assessed for three variant sectors with suitable evaluation metrics.

scholarly journals A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ming Zhao ◽  
Qingjun Zeng ◽  
Ming Chang ◽  
Qian Tong ◽  
Jiafu Su
Keyword(s):  
Prediction Model ◽  
Empirical Research ◽  
Customer Value ◽  
Customer Relationship ◽  
Churn Prediction ◽  
Customer Churn ◽  
Data Mining Algorithms ◽  
Customer Churn Prediction ◽  
Telecom Industry ◽  
Logistic Regression Algorithm

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain “high-value” customers, and continue to provide customers with “value” and reduce the cost of maintaining customers.

scholarly journals Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping

2021 ◽  
Vol 11 (11) ◽  
pp. 4742
Author(s):  
Tianpei Xu ◽  
Ying Ma ◽  
Kangchul Kim
Keyword(s):  
Ensemble Learning ◽  
Machine Learning Algorithms ◽  
Customer Relationship ◽  
Prediction System ◽  
Churn Prediction ◽  
Customer Churn ◽  
Feature Grouping ◽  
Recognition Systems ◽  
The Cost ◽  
Better Than

In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a stacking model with two levels, and the three outputs of the second level are used for soft voting. Feature construction of the churn dataset includes equidistant grouping of customer behavior features to expand the space of features and discover latent information from the churn dataset. The original and new churn datasets are analyzed in the stacking ensemble model with four evaluation metrics. The experimental results show that the proposed customer churn predictions have accuracies of 96.12% and 98.09% for the original and new churn datasets, respectively. These results are better than state-of-the-art churn recognition systems.

Feature-selection-based dynamic transfer ensemble model for customer churn prediction

2014 ◽  
Vol 43 (1) ◽  
pp. 29-51 ◽  
Author(s):  
Jin Xiao ◽  
Yi Xiao ◽  
Anqiang Huang ◽  
Dunhu Liu ◽  
Shouyang Wang
Keyword(s):  
Feature Selection ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction
Sign in / Sign up

Export Citation Format

Share Document