Churn prediction by finding most influential nodes in social network

Pagare, Reena; Khare, Akhil

doi:10.1109/cast.2016.7914942

Cited by 10 publications

(10 citation statements)

References 10 publications

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“…Many researchers have studied the impact of a family or a friend leaving the same telecom company on a customer's churn decision [8,33]. That is because of the increase in call price between two customers with different voice provider.…”

Section: New Customer Churn Model Variablesmentioning

confidence: 99%

“…Some studies have realised the impact of social network information on churn prediction. For instance, reference [8] predicted customer churn by using customer information and their social network information. Their dataset was from the Pokec social network (http://snap.stanford.edu/data/soc-pokec.html, accessed on 23 June 2021) and the call details of customers issued from the network over an interval of six months.…”

Section: New Customer Churn Model Variablesmentioning

confidence: 99%

“…The current churn prediction models exclude location and language factors and that causes geographical and cultural sampling errors [8].…”

mentioning

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%

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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“…Previous studies of information propagation prediction in social networks refer to one of the following three tasks: predicting information popularity [11][12][13][14], foretelling user influence [15][16][17][18], and divining information diffusion paths (links) [19][20][21][22]. Some of the literature focuses on the user influence in the social analysis [15,17].…”

Section: Related Workmentioning

confidence: 99%

“…e derivation p(V, W|(y t |y 1:t− 1 )) in equation ( 18) is the prediction probability density function of the observation sequence y 1:t− 1 , which obeys the Gaussian distribution with mean f t and variance Q t obtained by the forward Kalman filter algorithm. To simplify the operation, we convert equation (18) to its log-likelihood form:…”

Section: Maximum Likelihood Estimationmentioning

confidence: 99%

Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

Chen

Lan

Wan

et al. 2021

Discrete Dynamics in Nature and Society

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A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

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Churners Prediction Using Relational Classifier Based on Mining the Social Network Connection Structure

Alzubaidi

Al-Shamery

2020

Communications in Computer and Information Science

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Churn prediction by finding most influential nodes in social network

Cited by 10 publications

References 10 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

Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

Churners Prediction Using Relational Classifier Based on Mining the Social Network Connection Structure

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