scholarly journals Literature Review of Using DWHBI Approaches to Predict and Reduce Customer Churn in Telecommunications Industry

2020 ◽  
pp. 176-180
Author(s):  
K. Prakash Krishnan ◽  
A. Kumar Kombaiya
Keyword(s):  
Service Providers ◽  
High Volume ◽  
Telecommunications Industry ◽  
Vital Role ◽  
Global Market ◽  
Customer Requirements ◽  
Churn Prediction ◽  
Field Service ◽  
Customer Churn ◽  
Telecom Industry

In recent days, telecom industry plays a vital role in our daily life. During corona period entire world depends on the telecom services domain. But telecome industry has been facing many surivival problems in the global market since last 10 years due to heavy competition between competitors. To stand in this field, service providers have to understand the complete customer requirements and provide the efficient services to stop the customer movement from one network to another network. Customer churn is one of the most critical problem faced by the telecom industry. In this industry, it is more expensive to bring the new customers as compared to retain the existing customers. The objective of customer churn prediction is to find the subscribers that are ready to move from the current service provider in advance. Churn prediction allows the service providers to offer new benefits and campaign offers to retain the existing customer in the same network. Technically this term would be call it as ‘Win back Situation’ in telecom industry. The high volume of data generated by telecom industry , with the help of data warehosuing and business intelligence implementation would become the main asset for predicting the customer churn. To prevent the churn many models and methods are used by researchers. In this paper, we reviewed different mining methods and the most popular algorithms which used in telecom industry. But this model is not only for telecom domain, it can be implement for other domains which has highly depends on customer interactions.

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 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.

scholarly journals Impact of Proactive Postsales Service and Cross-Selling Activities on Customer Churn and Service Calls

2019 ◽  
Vol 23 (1) ◽  
pp. 53-69 ◽  
Author(s):  
Jan U. Becker ◽  
Martin Spann ◽  
Christian Barrot
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Telecommunications Industry ◽  
Negative Consequences ◽  
Mediating Effects ◽  
Service Failures ◽  
Customer Churn ◽  
Large Scale Field ◽  
Media Types ◽  
The Impact

In recent years, service providers have identified the proactive postsales service (PPS) as a viable measure for preempting service failures and their negative consequences. Due to the high costs associated with PPSs, companies are looking for ways to increase their efficiency. To understand how companies can increase their revenues and lower their costs, this study investigates how cross-selling activities and different media types affect the impact of a PPS on inbound service calls and customer churn. Based on a large-scale field experiment in the telecommunications industry, as well as a controlled lab experiment, the results demonstrate the overall effectiveness of the PPS and indicate two mediating effects. While the effect of cross-selling on customer churn and service calls is mediated by the customers’ uncertainty regarding the company’s motives, it is the customers’ perception of privacy invasion that mediates the influence of the contact medium on the effectiveness of the PPS. Our finding that PPS contacts have to be clear in their message and should not be perceived as invasive is an indication of the importance of service-(post)sales ambidexterity.

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 Projection pursuit random forest using discriminant feature analysis model for churners prediction in telecom industry

2020 ◽  
Vol 10 (2) ◽  
pp. 1406
Author(s):  
Asia Mahdi Naser alzubaidi ◽  
Eman Salih Al-Shamery
Keyword(s):  
Discriminant Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Lift Coefficient ◽  
Telecommunications Industry ◽  
Feature Analysis ◽  
Support Vector ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction

A major and demanding issue in the telecommunications industry is the prediction of churn customers. Churn describes the customer who is attrite from one Telecom service provider to competitors searching for better services offers. Companies from the Telco sector frequently have customer relationship management offices it is the main objective in how to win back defecting clients because preserve long-term customers can be much more beneficial to a company than gain newly recruited customers. Researchers and practitioners are paying great attention and investing more in developing a robust customer churn prediction model, especially in the telecommunication business by proposed numerous machine learning approaches. Many approaches of Classification are established, but the most effective in recent times is a tree-based method. The main contribution of this research is to predict churners/non-churners in the Telecom sector based on project pursuit Random Forest (PPForest) that uses discriminant feature analysis as a novelty extension of the conventional Random Forest approach for learning oblique Project Pursuit tree (PPtree). The proposed methodology leverages the advantage of two discriminant analysis methods to calculate the project index used in the construction of PPtree. The first method used Support Vector Machines (SVM) as a classifier in the construction of PPForest to differentiate between churners and non-churners customers. The second method is a Linear Discriminant Analysis (LDA) to achieve linear splitting of variables node during oblique PPtree construction to produce individual classifiers that are robust and more diverse than classical Random Forest. It found that the proposed methods enjoy the best performance measurements e.g. Accuracy, hit rate, ROC curve, Gini coefficient, Kolmogorov-Smirnov statistic and lift coefficient, H-measure, AUC. Moreover, PPForest based on direct applied of LDA on the raw data delivers an effective evaluator for the customer churn prediction model.

scholarly journals Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique

2020 ◽  
Vol 9 (3) ◽  
pp. 475-482
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Random Forest ◽  
Prediction Model ◽  
Classification Accuracy ◽  
Churn Prediction ◽  
Customer Churn ◽  
Customer Churn Prediction ◽  
Telecom Industry ◽  
Fierce Competition

Customer Churn Prediction has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.

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.

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