A Prudent Based Approach for Customer Churn Prediction

Amin, Adnan; Rahim, Faisal; Ramzan, Muhammad; Anwar, Sajid

doi:10.1007/978-3-319-18422-7_29

Cited by 12 publications

(2 citation statements)

References 21 publications

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“…Amin et al [16] developed a prudent churn prediction model that will generate an alert to the decision-makers whenever there is a new case that is not covered by the KB system. The developed approach uses ripple down rule (RDR) classifier to build the KB.…”

Section: Related Workmentioning

confidence: 99%

WisdomModel: convert data into wisdom

Mahmood

Abdullah

2021

ACI

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PurposeTraditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.Design/methodology/approachThe proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.FindingsThe authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.Originality/valueThis research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.

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Section: Related Workmentioning

confidence: 99%

WisdomModel: convert data into wisdom

Mahmood

Abdullah

2021

ACI

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“…A three-phase customer churn prediction technique was presented in [ 18 ]. The first phase included a supervised feature selection procedure, while the second implied the Knowledge Based System (KBS) definition through Ripple Down Rule (RDR) learner.…”

Section: Related Workmentioning

confidence: 99%

Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory

Kostic

Simić

Kostic

2020

Entropy

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Due to telecommunications market saturation, it is very important for telco operators to always have fresh insights into their customer’s dynamics. In that regard, social network analytics and its application with graph theory can be very useful. In this paper we analyze a social network that is represented by a large telco network graph and perform clustering of its nodes by studying a broad set of metrics, e.g., node in/out degree, first and second order influence, eigenvector, authority and hub values. This paper demonstrates that it is possible to identify some important nodes in our social network (graph) that are vital regarding churn prediction. We show that if such a node leaves a monitored telco operator, customers that frequently interact with that specific node will be more prone to leave the monitored telco operator network as well; thus, by analyzing existing churn and previous call patterns, we proactively predict new customers that will probably churn. The churn prediction results are quantified by using top decile lift metrics. The proposed method is general enough to be readily adopted in any field where homophilic or friendship connections can be assumed as a potential churn driver.

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Churn prediction in Turkey's telecommunications sector: A proposed multiobjective–cost‐sensitive ant colony optimization

Özmen

Aydoğan

Delice

et al. 2019

WIREs Data Min & Knowl

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Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot be ignored. For this reason, studies on cost‐sensitive classification approaches have gained importance in recent years. The characteristics of telecommunications datasets such as high dimensionality and imbalance are making it difficult to achieve the desired performance for churn prediction. By taking this into consideration, we propose a multiobjective–cost‐sensitive ant colony optimization (MOC‐ACO‐Miner) approach which integrates the cost‐based nondominated sorted genetic algorithm feature selection and multiobjective ACO based cost‐sensitive learning. MOC‐ACO‐Miner is applied to one of Turkey's top 100 information technology companies for customer churn‐prediction. Finally, experiments find out that the model performs quite well with the area under receiver operating characteristic curve values of 0.9998 for predicting churners and therefore it can be beneficial for the highly competitive telecommunications sector. This article is categorized under: Algorithmic Development > Association Rules Application Areas > Industry Specific Applications Technologies > Prediction Technologies > Data Preprocessing

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A Prudent Based Approach for Customer Churn Prediction

Cited by 12 publications

References 21 publications

WisdomModel: convert data into wisdom

WisdomModel: convert data into wisdom

Social Network Analysis and Churn Prediction in Telecommunications Using Graph Theory

Churn prediction in Turkey's telecommunications sector: A proposed multiobjective–cost‐sensitive ant colony optimization

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