Getting what you paid for: Fighting wireless customer churn with rate plan optimization

Wong, Ken Kwong-Kay

doi:10.1057/dbm.2011.8

Cited by 16 publications

(12 citation statements)

References 21 publications

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“…This paper is to understand what factors should be taken into consideration by the leader with the aim of qualitative methods. Wong [7] said the breakaway study was incomplete because it could be affected by a wide range of factors. Employee demographics are often changed, so that employees' demographics are constantly monitored.…”

Section: Cohesive Groupsmentioning

confidence: 99%

Prediction of Workplace Absenteeism Time Using Machine Learning

Choi*¹

2019

IJITEE

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Absenteeism in the workplace is a significant cause of lost productivity of the organization and the root cause of the company's performance to many employers. Managing absenteeism is inevitable, but making sudden changes without knowing the cause of the problem is a terrible mistake. This paper aims to develop a reliable workplace absenteeism prediction model using machine learning and natural language processing techniques to aid employers with analyzation of given minimal available information about the employees’ demographics. ‘Distance from residence to work,’ ‘disciplinary failure’ and ‘weight’ was negatively associated with absenteeism time in hours. ‘Age,’ ‘son,’ and ‘height’ were positively associated with absenteeism time in hours

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Section: Cohesive Groupsmentioning

confidence: 99%

Prediction of Workplace Absenteeism Time Using Machine Learning

Choi*¹

2019

IJITEE

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“…This paper is to understand what factors should be taken into consideration by the leader with the aim of qualitative methods. Wong [9] said the breakaway study was incomplete because it could be affected by a wide range of factors. Because wireless carrier data is not available, some factors, such as income level, training background, and marital status, are not included.…”

Section: The Determinants Of Churnmentioning

confidence: 99%

Prediction of Churn in Telecom Service: Exploring Call Behaviors and using Machine Learning

Choi*

2019

IJITEE

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Churn has a significant impact on mobile network operators and telecommunications service providers. Many studies on churn have been reported, but no one can say that they can create universal human tools for predicting churn or that we can see all the reasons for it. The purpose of this study is to derive the call behavior factors of churning customers and to find ways to reduce the churn of target customers who exhibit these call behaviors. For this, this study uses decision tree and machine learning for the prediction of churn in telecom service. Based on the analysis results, first, the fact that the total number of customers who have more than 316.7 in churn shows that the higher the number of calls, the higher the chance of churn. Second, among customers with total day minutes above 316.7, those with customer service calls above 8.5 show a high likelihood of churn among complaining customers. The overall accuracy is 91.4%. Among the customers who predicted not to be churned, the accuracy that would not be churned was 92.87%, and the accuracy that was churned was 78.4% among the customers predicted to be churned

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“…Wineries in the USA have attempted to combat the regular substitution of wine brands in repertoire markets by applying a customer retention tool that is more consistent with subscription services, such as telecommunications and sports club members (McDonald et al, 2014;Wong, 2011). Successful management of customers in a subscription model can lead to lower churn rates, but still requires continuous acquisition in order for the business to grow (Castéran et al, 2017;Hassouna et al, 2015;Riebe et al, 2014).…”

Section: Attrition and Retentionmentioning

confidence: 99%

Defecting wine club members: an exploratory study

Newton

Nowak

Kelkar

2018

IJWBR

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Purpose -The purpose of this study is to investigate the range of explanations for why wine club members defect and move on.Design/methodology/approach -This quantitative research study uses data from US wine consumers, gathered through an online survey of 399 former wine club members who had quit their membership in the recent past. Consistent with literature on customer churn rates in subscription markets, data are analyzed using descriptive statistics, factor analysis, hierarchical multiple regression and analysis of variance.Findings -The results reported by respondents indicate that higher levels of perceived product quality, fair value in pricing, variety seeking and commitment to customer service at the beginning and at the end of a wine club membership lead to higher levels of customer satisfaction and a desire to recommend the club to others even after quitting. Though variety seeking is more commonplace among experienced wine drinkers, the good news for wineries is that consumers are more likely to recommend a wine club to others if at least a year has passed after they decided to quit.Practical implications -The results provide implications for wine club managers seeking to improve wine club retention with suggested means for mitigating the rate of customer attrition.Originality/value -This paper presents original research addressing a variety of reasons why wine club members quit. The extant research has found that factors such as product quality, fair pricing, service commitments and variety-seeking behavior affect members' satisfaction with their wine club, as well as their desire to recommend it to others. The authors have attempted to combine all these factors into a single study to gain insight into wine club members' switching behavior, and to find out what the wineries can do to improve customer loyalty.

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Getting what you paid for: Fighting wireless customer churn with rate plan optimization

Cited by 16 publications

References 21 publications

Prediction of Workplace Absenteeism Time Using Machine Learning

Prediction of Workplace Absenteeism Time Using Machine Learning

Prediction of Churn in Telecom Service: Exploring Call Behaviors and using Machine Learning

Defecting wine club members: an exploratory study

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