scholarly journals Incorporating textual information in customer churn prediction models based on a convolutional neural network

2020 ◽  
Vol 36 (4) ◽  
pp. 1563-1578 ◽  
Author(s):  
Arno De Caigny ◽  
Kristof Coussement ◽  
Koen W. De Bock ◽  
Stefan Lessmann
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Prediction Models ◽  
Churn Prediction ◽  
Textual Information ◽  
Customer Churn ◽  
Customer Churn Prediction

Distributed model for customer churn prediction using convolutional neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muhammad Usman Tariq ◽  
Muhammad Babar ◽  
Marc Poulin ◽  
Akmal Saeed Khattak
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
False Negative ◽  
Accuracy Score ◽  
Churn Prediction ◽  
Data Set ◽  
Content Type ◽  
Customer Churn ◽  
Proposed Model ◽  
Customer Churn Prediction

Purpose The purpose of the proposed model is to assist the e-business to predict the churned users using machine learning. This paper aims to monitor the customer behavior and to perform decision-making accordingly. Design/methodology/approach The proposed model uses the 2-D convolutional neural network (CNN; a technique of deep learning). The proposed model is a layered architecture that comprises two different phases that are data load and preprocessing layer and 2-D CNN layer. In addition, the Apache Spark parallel and distributed framework is used to process the data in a parallel environment. Training data is captured from Kaggle by using Telco Customer Churn. Findings The proposed model is accurate and has an accuracy score of 0.963 out of 1. In addition, the training and validation loss is extremely less, which is 0.004. The confusion matric results show the true-positive values are 95% and the true-negative values are 94%. However, the false-negative is only 5% and the false-positive is only 6%, which is effective. Originality/value This paper highlights an inclusive description of preprocessing required for the CNN model. The data set is addressed more carefully for the successful customer churn prediction.

scholarly journals Customer Churn Prediction using Predictive Analytics in Telecommunication Market: A Review

2019 ◽  
pp. 97-105
Keyword(s):  
Performance Metrics ◽  
Prediction Models ◽  
Service Providers ◽  
Predictive Analytics ◽  
Churn Prediction ◽  
Customer Churn ◽  
Telecommunication Market ◽  
Company Limited ◽  
The Face ◽  
Customer Churn Prediction

In the face of extreme competitive telecommunication market, the cost of acquiring new customer is much more expensive than to retain the existing customer. Therefore, it has become imperative to pay much attention towards retaining the existing customers in order to get stabilize in market comprised of vibrant service providers. In current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. This article reviews the customer churn prediction problem, factors escalating the phenomena, prediction through predictive analytics, steps for processing of predictive analytics and evaluation of performance metrics for various churn prediction models are surveyed. Moreover, the CRM data from Pakistan Telecommunication Company limited as case study to discuss the process of data mining and predictive analytics for customer churn prediction.

Customer Churn Prediction Models For PT. XYZ Insurance

2020 ◽  
Author(s):  
Tuga Mauritsius ◽  
Kristianto ◽  
Reagen Y. Sayoga ◽  
Natanael Alamas ◽  
Marcelina Anggraeni ◽  
...  
Keyword(s):  
Prediction Models ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

scholarly journals A Critical Examination of Different Models for Customer Churn Prediction using Data Mining

2019 ◽  
Vol 8 (6S3) ◽  
pp. 850-854
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Critical Examination ◽  
Churn Prediction ◽  
Customer Churn ◽  
Learning Techniques ◽  
Customer Churn Prediction ◽  
Using Data

Due to competition between online retailers, the need for providing improved customer service has grown rapidly. In addition to reduction in sales due to loss of customers, more investments are needed to be done to attract new customers. Companies now are working continuously to improve their perceived quality by way of giving timely and quality service to their customers. Customer churn has become one of the primary challenges that many firms are facing nowadays. Several churn prediction models and techniques are proposed previously in literature to predict customer churn in areas such as finance, telecom, banking etc. Researchers are also working on customer churn prediction in e-commerce using data mining and machine learning techniques. In this paper, a comprehensive review of various models to predict customer churn in e-commerce data mining and machine learning techniques has been presented. A critical review of recent research papers in the field of customer churn prediction in e-commerce using data mining has been done. Thereafter, important inferences and research gaps after studying the literature are presented. Finally, the research significance and concluding remarks are described in the end.

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