Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model

Xu, Jiabing; Liu, Jiarui; Yao, Tianen; Li, Yang

doi:10.1089/big.2021.0365

Cited by 7 publications

(1 citation statement)

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“…Decision tree was also used by Shuai and Huang to predict the client churn for the customer relationship management system [4]. Xu, Liu and Yao used the backpropagation neural network to build the prediction model [5]. The goal of Kamalraj and Malathi's study was to better understand churn prediction through the use of data mining techniques [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Data mining models in telecom churn prediction

Xing

2024

ACE

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Customer attrition has become one of the biggest problems facing the telecommunications industry due to its fast growth. According to telecom studies, acquiring new customers is more expensive than keeping current ones. Telecom companies may use the information gleaned from telecom data to understand the causes behind customer churn and take steps to maintain their current clientele. This study explores the popular data mining methods for spotting trends in loss of clients. Principal component analysis is used in the survey to reduce the dimension of the attributes. Some conclusions about the relationship between costumer usage data their churn can be summarized through the exploratory data analysis. And three prediction techniques (Logistic Regression, SVM Regression, Random Forest Regression) are applied in the customer churn prediction. This paper compares the accuracy and performance of these models. The result shows, among these models, SVM regression has the highest accuracy. It has an accuracy of 0.92 and a precision of 0.48.

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Section: Literature Reviewmentioning

confidence: 99%