Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry

Gordini, N; Veglio, Valerio

doi:10.1016/j.indmarman.2016.08.003

Cited by 123 publications

(73 citation statements)

References 53 publications

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“…Rough Set Theory based on Genetic algorithms produced efficient decision rules as compared to other rule generation mechanisms named Exhaustive Algorithm, Covering Algorithm and the LEM2 algorithm for churn and non-churn classification [25]. A churn prediction model based on AUC parameter selection technique is proposed which has shown good performance in the case of noisy nonlinear business customer's dataset [26].…”

Section: Related Workmentioning

confidence: 99%

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Anjum¹,

Usman²,

Zeb³

et al. 2017

ijacsa

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Abstract-Companies are investing more in analytics to obtain a competitive edge in the market and decision makers are required better identification among their data to be able to interpret complex patterns more easily. Alluring thousands of new customers is worthless if an equal number is leaving. Business Intelligence (BI) systems are unable to find hidden churn patterns for the huge customer base. In this paper, a decision support system has been proposed, which can predict the churning behaviour of a customer efficiently. We have proposed a procedure to develop an analytical system using data mining as well as machine learning techniques C5, CHAID, QUEST, and ANN for the churn analysis and prediction for the telecommunication industry. Prediction performance can be significantly improved by using a large volume and several features from both Business Support Systems (BSS) and Operations Support Systems (OSS). Extensive experiments are performed; marginal increases in predictive performance can be seen by using a larger volume and multiple attributes from both Telco BSS and OSS data. From the results, it is observed that using a combination of techniques can help to figure out a better and precise churn prediction model.

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Section: Related Workmentioning

confidence: 99%

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Anjum¹,

Usman²,

Zeb³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques have also been compared for customer churn prediction (Vafeiadis, Diamantaras, Sarigiannidis, & Chatzisavvas, 2015). Many researchers have tried to improve prediction performance and integrate different kinds of classification methods such as SVMs (Gordini & Veglio, 2017;Yu, Guo, Guo, & Huang, 2011), random forest, and neural network (NN) methods (Han et al, 2006). Tsai and Chen (2010) used association rules to select prediction factors, and then they constructed a churn prediction model by NNs and decision trees for multimedia on demand customers.…”

Section: Customer Churn Predictionmentioning

confidence: 99%

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

et al. 2019

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In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation‐based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation‐based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.

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“…Information Technology and Computer Science, 2019, 6, 1-8 the parameter-selection was optimized by SVMauc which improved the prediction performance. A meta-heuristic based approach was used for CCP by Ammar et al [25] in which a hybridized form of firefly algorithm approach was used as a classifier. The highest light intensity of every firefly was compared with each other to find out which one has the highest light intensity.…”

Section: Related Workmentioning

confidence: 99%

Improving the Reliability of Churn Predictions in Telecommunication Sector by Considering Customer Region

Zhou¹,

Boateng²,

Amoh³

et al. 2019

IJITCS

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Prediction of customer churn has been one of the most interesting and challenging tasks facing most telecommunication companies recently. For the past decade, researchers together with practitioners have been working and designing models that can correctly make more accurate customer churn predictions (CCP). However, most of these models have less accuracy than expected which is hugely affecting the intended purpose. Consequently, most of these CCP models add little or nothing to the revenue growth of telecommunication industries. This work aims at improving the reliability of CCP in the telecommunication sector. To achieve this objective, a new attribute to be factored in CCP, known as the regional churn rate (RCR), is presented. Basically, RCR gives an idea about the rate of churning in a particular locality or region. Thus, a prediction model with a more accurate CCP using a support vector machine (SVM) classifier is proposed. The performance of the proposed model is critically evaluated using five metrics i.e. misclassification error, precision, recall, specificity and f-measure. At the same time, the performance of the proposed classifier (CCP with RCR) is compared with another SVM classifier which doesn't consider RCR (CCP without RCR). Results show that the proposed model which considers the RCR of a customer's location gives the highest accuracies for four performance metrics i.e. precision, recall, misclassification error and f-measure. Therefore, the proposed SVM-based CCP model gives a more clear indication as to whether a customer is a potential churner or not.

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Customers churn prediction and marketing retention strategies. An application of support vector machines based on the AUC parameter-selection technique in B2B e-commerce industry

Cited by 123 publications

References 53 publications

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Optimizing Coverage of Churn Prediction in Telecommunication Industry

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

Improving the Reliability of Churn Predictions in Telecommunication Sector by Considering Customer Region

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