Employee churn prediction

Saradhi, V. Vijaya; Palshikar, Girish Keshav

doi:10.1016/j.eswa.2010.07.134

Cited by 142 publications

(79 citation statements)

References 14 publications

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“…Several methodologies and approaches have been proposed which mainly leverage both static and dynamic analysis for churn prediction modeling. Although, Churn analysis problem is an alarming issue for various domains such as Credit cards accounts [17], Banks & Financial Services [27], Human resource management [22], Insurance & subscription services [25], games [26] and social networks [28], this section represents various related studies about customer churn prediction modeling in telecommunication sector.…”

Section: Churn Prediction Modelingmentioning

confidence: 99%

A Prudent Based Approach for Customer Churn Prediction

Amin

Rahim

Ramzan

et al. 2015

Beyond Databases, Architectures and Structures

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Abstract. This study contributes to formalize a three phase customer churn prediction technique. In the first phase, a supervised feature selection procedure is adopted to select the most relevant subset of features by laying-off the redundancy and increasing the relevance that leads to reduced and highly correlated features set. In the second phase, a knowledge based system (KBS) is built through Ripple Down Rule (RDR) learner which acquires knowledge about seen customer churn behavior and handles the problem of brittle in churn KBS through prudence analysis that will issue a prompt to the decision maker whenever a case is beyond the maintained knowledge in the knowledge database. In the final phase, a technique for Simulated Expert (SE) is proposed to evaluate the Knowledge Acquisition (KA) in KB system. Moreover, by applying the proposed approach on publicly available dataset, the results show that the proposed approach can be a worthy alternate for churn prediction in telecommunication industry.

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Section: Churn Prediction Modelingmentioning

confidence: 99%

A Prudent Based Approach for Customer Churn Prediction

Amin

Rahim

Ramzan

et al. 2015

Beyond Databases, Architectures and Structures

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“…Some research has shown that this costs between 6 (Verbeke et al, 2011) and 12 times that of retaining the existing customer (Torkzadeh et al, 2006); Secondly, lost customers have a negative effect on the company's reputation and impact negatively on the brand's image. Churners tend to give negative feedback about the company, which may influence potential customers (Saradhi and Palshikar, 2011). Therefore, predicting policy cancellation before the renewal date is a critical point for most companies.…”

Section: Introductionmentioning

confidence: 99%

The impact of geographical factors on churn prediction: an application to an insurance company in Madrid's urban area

Llave

Hernández

Angulo

2018

Scandinavian Actuarial Journal

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Geography has previously been noted as a decisive factor in business literature. This paper provides evidence of the significant role geography plays in customer lapse behaviour in an urban environment. This novel approach is based on the idea that the customers who cancel all policies and leave the company are not randomly distributed; rather, a mimetic performance of close individuals is noted. The physical proximity of the customer to the geographical focus (strategical centre, as insurance offices) and the interaction with nearby customer are spatial factors that increase (or decrease) the probability of churning. An empirical analysis using more than 7,000 spatially georeferenced offline customers of a Spanish insurance company in the urban area of Madrid (Spain) demonstrated that the customer's proximity to offices of such insurance company under study decreases the probability of churning, whereas high lapse risk was detected in customers in the surroundings of the company's competitor branches. In addition, we identified spatial autocorrelation in churn probability, thus demonstrating that the probability of churn of a customer increases if nearby customers churn.

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“…Churn prediction has been widely researched in the fields of telecom, finance, retail, pay TV and banking, as shown by the extensive literature review given by [55], [58]. It has also been studied in e-commerce [60], [61] and even in terms of employee retention [51].…”

Section: Introductionmentioning

confidence: 99%

Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles

Periáñez¹,

Alain²,

Guitart³

et al. 2016

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Abstract-Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn.Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results.In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.

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Employee churn prediction

Cited by 142 publications

References 14 publications

A Prudent Based Approach for Customer Churn Prediction

A Prudent Based Approach for Customer Churn Prediction

The impact of geographical factors on churn prediction: an application to an insurance company in Madrid's urban area

Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles

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