Significance-Based Feature Extraction for Customer Churn Prediction Data in the Telecom Sector

2019 ◽  
Vol 16 (8) ◽  
pp. 3428-3431
Author(s):  
Kamya Eria ◽  
Booma Poolan Marikannan
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Real Time ◽  
Missing Values ◽  
Service Providers ◽  
Prediction Errors ◽  
Churn Prediction ◽  
Logistic Regression Models ◽  
Customer Churn ◽  
Churn Analysis

The telecom industry is saturated with many service providers competing for highly rational customers. The current big data and highly technological era calls for real-time churn analysis and decision making which has also been highlighted in previous studies. However, telecom data is highly dimensional in nature thus when this is coupled with this big data era increases the computational and processing costs. Therefore, this complexity and dimensionality of telecom data coupled with the current need for near or real-time churn analysis demands feature selection-based models that efficiently consider the most relevant variables in explaining customer churn behaviors. This study proposes a feature extraction-based churn prediction model that concentrates on the most relevant features with significant discriminatory power for churn. The data has been reduced on the basis of missing values and irrelevant variables. Irrelevant variables were first identified by use of Random Forest and Logistic Regression models. The findings of the study provide churn analysts with insights about the prediction errors to consider and minimize in their future churn analyses. It also contributes to reducing computational costs incurred by churn analysts working with big data in their churn prediction and analysis.

Feature intersection for agent-based customer churn prediction

2019 ◽  
Vol 53 (3) ◽  
pp. 318-332
Author(s):  
Sandhya N. ◽  
Philip Samuel ◽  
Mariamma Chacko
Keyword(s):  
Service Providers ◽  
Mobile User ◽  
Telecommunication Service ◽  
Agent Based Model ◽  
Churn Prediction ◽  
Content Type ◽  
Agent Based ◽  
User Behaviour ◽  
Customer Churn ◽  
Customer Churn Prediction

Purpose Telecommunication has a decisive role in the development of technology in the current era. The number of mobile users with multiple SIM cards is increasing every second. Hence, telecommunication is a significant area in which big data technologies are needed. Competition among the telecommunication companies is high due to customer churn. Customer retention in telecom companies is one of the major problems. The paper aims to discuss this issue. Design/methodology/approach The authors recommend an Intersection-Randomized Algorithm (IRA) using MapReduce functions to avoid data duplication in the mobile user call data of telecommunication service providers. The authors use the agent-based model (ABM) to predict the complex mobile user behaviour to prevent customer churn with a particular telecommunication service provider. Findings The agent-based model increases the prediction accuracy due to the dynamic nature of agents. ABM suggests rules based on mobile user variable features using multiple agents. Research limitations/implications The authors have not considered the microscopic behaviour of the customer churn based on complex user behaviour. Practical implications This paper shows the effectiveness of the IRA along with the agent-based model to predict the mobile user churn behaviour. The advantage of this proposed model is as follows: the user churn prediction system is straightforward, cost-effective, flexible and distributed with good business profit. Originality/value This paper shows the customer churn prediction of complex human behaviour in an effective and flexible manner in a distributed environment using Intersection-Randomized MapReduce Algorithm using agent-based model.

scholarly journals To Optimize Network Call Drops and Enhances Real Time (CDR) Data Handling with Security

2020 ◽  
Author(s):  
Ashok G V ◽  
Dr.Vasanthi Kumari P
Keyword(s):  
Real Time ◽  
Service Providers ◽  
Large Data ◽  
Network Capacity ◽  
Data Set ◽  
Satisfaction Index ◽  
Customer Churn ◽  
Trend Forecasting ◽  
Telecom Networks

The telecom networks generate multitudes and large sets of data related to networks, applications, users, network operations and real time call processing (Call Detail Record (CDR)). This large data set has the capability to give valuable business insights - for example, real-time user quality of service, network issues, call drop issues, customer satisfaction index, customer churn, network capacity forecast and many more revenue impacting insights. As even setting up of more towers for better coverage would also directly affect the health of habitants around. In this paper, the overall condition of call drops has been reviewed and possible ways to minimize the spectacles of network call drops. Applied Linear Regression algorithm which is used type of predictive analysis. Three major uses for regression analysis Determining the strength of predictors, Forecasting an effect and Trend forecasting. This paper gives to telecom service providers to improve their networks and minimize the network call drops with security. Deliver quality of services to their subscribers using the advanced technologies with accurate algorithms.

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.

Research on real time feature extraction method for complex manufacturing big data

2016 ◽  
Vol 99 (5-8) ◽  
pp. 1101-1108 ◽  
Author(s):  
Xianguang Kong ◽  
Jiantao Chang ◽  
Meng Niu ◽  
Xiaoyu Huang ◽  
Jihu Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Real Time ◽  
Extraction Method ◽  
Feature Extraction Method

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 Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Rodan ◽  
Ayham Fayyoumi ◽  
Hossam Faris ◽  
Jamal Alsakran ◽  
Omar Al-Kadi
Keyword(s):  
Negative Correlation ◽  
Service Providers ◽  
Multilayer Perceptrons ◽  
Comparison Study ◽  
Churn Prediction ◽  
Customer Churn ◽  
Negative Correlation Learning ◽  
Telecommunication Companies ◽  
Correlation Learning ◽  
Customer Churn Prediction

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

scholarly journals Next-Wave of E-commerce: Mobile Customers Churn Prediction using Machine Learning

2021 ◽  
Vol 5 (2) ◽  
pp. 62-72
Author(s):  
Asif Yaseen
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Service Providers ◽  
Mobile Commerce ◽  
Personal Digital Assistants ◽  
Churn Prediction ◽  
Telecommunication Sector ◽  
Feature Selection Technique ◽  
Customer Churn ◽  
Feature Selection Approach

With the swift increase of mobile devices such as personal digital assistants, smartphones and tablets, mobile commerce is broadly considered to be a driving force for the next wave of ecommerce. The power of mobile commerce is primarily due to the anytime-anywhere connectivity and the use of mobile technology, which creates enormous opportunities to attract and engage customers. Many believe that in an era of m-commerce especially in the telecommunication business retaining customers is a big challenge. In the face of an extremely competitive telecommunication industry, the value of acquiring new customers is very much expensive than retaining the existing customer. Therefore, it has become imperative to pay much attention to retaining the existing customers in order to get stabilized in a market comprised of vibrant service providers. In the current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. In this study, we employed the feature selection technique to identify the most influencing factors in customer churn prediction. We adopt the wrapper-based feature selection approach where Particle Swarm Optimization (PSO) is used for search purposes and different classifiers like Decision Tree (DT), Naïve Bayes, k-NN and Logistic regression is used for evaluation purposes to assess the enactment on optimally sampled and abridged dataset. Lastly, it is witnessed through simulations that our suggested method accomplishes fairly thriving for forecasting churners and hence could be advantageous for exponentially increasing competition in the telecommunication sector.

A Big Data Query Optimization Framework for Telecom Customer Churn Analysis

2021 ◽  
pp. 475-484
Author(s):  
Aarti Chugh ◽  
Vivek Kumar Sharma ◽  
Manjot Kaur Bhatia ◽  
Charu Jain
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Customer Churn ◽  
Optimization Framework ◽  
Data Query ◽  
Churn Analysis
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