Customer loyalty prediction in multimedia Service Provider Company with K-Means segmentation and C4.5 algorithm

Moedjiono, Sardjoeni; Isak, Yosianus Robertus; Kusdaryono, Aries

doi:10.1109/iac.2016.7905717

Cited by 17 publications

(18 citation statements)

References 4 publications

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“…The use of K-Means segmentation technique and C4.5 algorithm to build a prediction model for customer loyalty in multimedia service provider was conducted. The integration of K-Means and C4.5 algorithm have yielded an increase of 79.33% accuracy prediction from the identified 69.23% accuracy with the C4.5 algorithm alone [27].…”

Section: Rank-based Variable Minimization Using Clustering Algorithmmentioning

confidence: 94%

Rank-Based Variable Minimization Using Clustering Algorithm

Delima¹,

Francisco²

2019

IJMLC

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This paper introduces variable minimization with the advent of data mining techniques, particularly the cluster analysis using the K-means algorithm. The results of the simulation process validated the actual ranking of the variables in each category from the 407 records of student-respondents. A noticeable decrease of variables in determining the instructional performance of teachers in the Caraga Region, Philippines is evident in the simulation results. The variables that were clustered by the algorithm that conforms to have the lowest rank based on the actual survey was removed. Results showed that there were a total of fourteen (14) variables removed from the thirty-item survey questionnaire. In this case, it only proved that like any other data mining algorithms, the cluster analysis, particularly with the K-Means algorithm, is also effective in determining what variables in the data set to omit based on the groupings with the lowest rank it generated.

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Section: Rank-based Variable Minimization Using Clustering Algorithmmentioning

confidence: 94%

Rank-Based Variable Minimization Using Clustering Algorithm

Delima¹,

Francisco²

2019

IJMLC

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“…Moedjionom et al [8] have predicted customer loyalty in a multimedia services company, offering many services to win the market. This research contribution is to use data related to the segmentation and splitting of potential customers based on the RFM model, then applying the classification, Proportion of accuracy in customer loyalty rating research.…”

Section: Related Workmentioning

confidence: 99%

Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study

Wassouf

Alkhatib²,

Salloum

et al. 2020

J Big Data

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IntroductionThe telecom sector is witnessing a massive increase in data, and by analyzing this massive data, telecom operators can manage and retain customers. It is also important for companies to be able to predict the amount of income they may receive from their active customers. For this purpose, they need models able to determine customer loyalty. The cost associated with customer gain is usually higher than the cost associated Abstract Given the growing importance of customer behavior in the business market nowadays, telecom operators focus not only on customer profitability to increase market share but also on highly loyal customers as well as customers who are churn. The emergence of big data concepts introduced a new wave of Customer Relationship Management (CRM) strategies. Big data analysis helps to describe customer's behavior, understand their habits, develop appropriate marketing plans for organizations to identify sales transactions and build a long-term loyalty relationship. This paper provides a methodology for telecom companies to target different-value customers by appropriate offers and services. This methodology was implemented and tested using a dataset that contains about 127 million records for training and testing supplied by Syriatel corporation. Firstly, customers were segmented based on the new approach (Time-frequencymonetary) TFM (TFM where: Time (T): total of calls duration and Internet sessions in a certain period of time. Frequency (F): use services frequently within a certain period. Monetary (M): The money spent during a certain period.) and the level of loyalty was defined for each segment or group. Secondly, The loyalty level descriptors were taken as categories, choosing the best behavioral features for customers, their demographic information such as age, gender, and the services they share. Thirdly, Several classification algorithms were applied based on the descriptors and the chosen features to build different predictive models that were used to classify new users by loyalty. Finally, those models were evaluated based on several criteria and derive the rules of loyalty prediction. After that by analyzing these rules, the loyalty reasons at each level were discovered to target them the most appropriate offers and services.

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“…An accuracy of 82.67% was depicted after applying the model to the imbalanced dataset. Also, a study of [35] used the K-Means segmentation technique and C4.5 algorithm to build a prediction model for customer loyalty in a multimedia service provider. The integration of K-Means and C4.5 algorithm have yielded an increase of 79.33% accuracy prediction from the identified 69.23% accuracy with the C4.5 algorithm alone.…”

Section: Introductionmentioning

confidence: 99%

“…With the advent of combining two or more models, an increase of prediction accuracy is evident [38] such that of [34] that obtained 82.67% and [35] with 79.33% after integrating hybridization than employing prediction with one algorithm alone.…”

Section: Introductionmentioning

confidence: 99%

Variable Reduction-based Prediction through Modified Genetic Algorithm

Delima¹,

Sison²,

Medina³

2019

IJACSA

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Due to the massive influence in the use of prediction models in different sectors of society, many researchers have employed hybrid algorithms to increase the accuracy level of the prediction model. The literature suggests that the use of Genetic Algorithms (GAs) can sufficiently improve the performance of other prediction models; thus, this study. This paper introduced a new avenue of prediction integrating GA with the novel Inversed Bi-segmented Average Crossover (IBAX) operator paired with rank-based selection function to the KNN algorithm. The 70% of data from 597 records of student-respondents in the evaluation of the faculty instructional performance from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as training set while the 30% was used for testing. The simulation result showed that the use of the proposed prediction model with the integration of the modified GA outperformed the KNN prediction model where GA with average crossover and roulette wheel selection function was used. The KNN where k value is three (3) was identified to be the optimal model for prediction with the 95.53% prediction accuracy compared to KNN with 1, 5, and 7 k values.

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Customer loyalty prediction in multimedia Service Provider Company with K-Means segmentation and C4.5 algorithm

Cited by 17 publications

References 4 publications

Rank-Based Variable Minimization Using Clustering Algorithm

Rank-Based Variable Minimization Using Clustering Algorithm

Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study

Variable Reduction-based Prediction through Modified Genetic Algorithm

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