A comparative study of social network classifiers for predicting churn in the telecommunication industry

Óskarsdóttir, María; Bravo, Cristián; Verbeke, Wouter; Sarraute, Carlos; Baesens, Bart; Vanthienen, Jan

doi:10.1109/asonam.2016.7752384

Cited by 12 publications

(7 citation statements)

References 37 publications

(45 reference statements)

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“…This finding denotes that a company is at risk of churning if a customer's relationship leaves the company. Moreover, references [35,38,39], used calling behaviour and network interaction (call length and number of calls) as churn predictors. Some studies have realised the impact of social network information on churn prediction.…”

Section: New Customer Churn Model Variablesmentioning

confidence: 99%

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An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021

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With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.

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Section: New Customer Churn Model Variablesmentioning

confidence: 99%

“…They combined call details from a social network with the information about the customers. Moreover, reference [38] used a relational learner to increase the performance of the churn prediction model. They analysed calling behaviour and network interaction.…”

Section: Used Influence Maximisationmentioning

confidence: 99%

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021

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“…Therefore, to retain customers, academics, as well as practitioners, find it crucial to build a churn prediction model that is as accurate as possible in order to minimize the risk of customer churn [8]. Also, researchers have confirmed that customer churn prediction models can improve a company's revenue and its reputation in the market [9], [10]. Reducing the rate of churn and retaining current customers are the most cost-effective marketing approaches that will maximize the shareholder's value [11], [12].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Customer Churn Prediction in E-Commerce Decision Support

et al. 2021

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Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.

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“…As a result of the comparisons made in accordance with the results obtained, the decision treebased model has been reported to provide better estimation results (Dahiya 2015). relational classifiers on 7 different data sets with an average of 1 million records as well as the comparison results have been given in the study (Oskarsdottir 2016).…”

Section: Studies Of Churn Analysis In Telecommunication Industrymentioning

confidence: 99%

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Kayaalp

2017

KSEJ

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companies are provided by the payments made by these customers periodically. It is very important to be able to keep customers satisfied in order to be able to sustain this revenue with the least expenditure cost. The objectives of this study are: • Reviewing the relevant studies about churn analysis on telecommunications industry presented in the last five years, particularly in the last two years, and introducing these up-to-date studies in the literature, • Determining the data mining methods frequently used in churn implementations, • Shedding a light on methods that can be used in further studies.

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A comparative study of social network classifiers for predicting churn in the telecommunication industry

Cited by 12 publications

References 37 publications

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

Deep Learning for Customer Churn Prediction in E-Commerce Decision Support

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