Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning
The application of data mining technique currently is widely used in supporting business activity, especially for insurance company which has to analyze a big number of customer data. The insurance company has to predict its new customer acquisition as well as maintain its existing customers. This p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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LPPM ISB Atma Luhur
2023-03-01
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| Series: | Jurnal Sisfokom |
| Subjects: | |
| Online Access: | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1507 |
| _version_ | 1797279824823189504 |
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| author | Riyan Pratama Muhammad Izman Herdiansyah Dedy Syamsuar Ahmad Syazili |
| author_facet | Riyan Pratama Muhammad Izman Herdiansyah Dedy Syamsuar Ahmad Syazili |
| author_sort | Riyan Pratama |
| collection | DOAJ |
| description | The application of data mining technique currently is widely used in supporting business activity, especially for insurance company which has to analyze a big number of customer data. The insurance company has to predict its new customer acquisition as well as maintain its existing customers. This paper is focused on how we support insurance companies, especially PT. XYZ, to analyze their customers’ characteristics data using the best data mining algorithm technique. The research aims to analyze existing customer data and to predict as well as find optimal patterns of how many of their customers will extend their insurance policies, and how many will not. We also explore the customer retention rate discovering the optimal solution for the company. We applied 4 different algorithms were applied, i.e. support vector machine algorithm, decision tree, k-NN, and random forest algorithm, comparing the results and finding a better solution. From the analysis, we found that the random forest algorithm provides better results in predicting the status of the insurance policy extension of current customers, with an accuracy rate of 91.08% and AUC value of 0.962. This result is quite good for PT XYZ, and could be enhanced in the future by applying a good strategy to increase their customer renewal ratio. |
| first_indexed | 2024-03-07T16:31:33Z |
| format | Article |
| id | doaj.art-6643213ff9a64a43925c60b83a4ac38b |
| institution | Directory Open Access Journal |
| issn | 2301-7988 2581-0588 |
| language | English |
| last_indexed | 2024-03-07T16:31:33Z |
| publishDate | 2023-03-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj.art-6643213ff9a64a43925c60b83a4ac38b2024-03-03T10:17:56ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882023-03-011219610410.32736/sisfokom.v12i1.1507770Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine LearningRiyan Pratama0Muhammad Izman Herdiansyah1Dedy Syamsuar2Ahmad Syazili3Universitas Bina DarmaUniversitas Bina DarmaUniversitas Bina DarmaUniversitas Bina DarmaThe application of data mining technique currently is widely used in supporting business activity, especially for insurance company which has to analyze a big number of customer data. The insurance company has to predict its new customer acquisition as well as maintain its existing customers. This paper is focused on how we support insurance companies, especially PT. XYZ, to analyze their customers’ characteristics data using the best data mining algorithm technique. The research aims to analyze existing customer data and to predict as well as find optimal patterns of how many of their customers will extend their insurance policies, and how many will not. We also explore the customer retention rate discovering the optimal solution for the company. We applied 4 different algorithms were applied, i.e. support vector machine algorithm, decision tree, k-NN, and random forest algorithm, comparing the results and finding a better solution. From the analysis, we found that the random forest algorithm provides better results in predicting the status of the insurance policy extension of current customers, with an accuracy rate of 91.08% and AUC value of 0.962. This result is quite good for PT XYZ, and could be enhanced in the future by applying a good strategy to increase their customer renewal ratio.http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1507data miningmachine learningcustomer retention |
| spellingShingle | Riyan Pratama Muhammad Izman Herdiansyah Dedy Syamsuar Ahmad Syazili Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning Jurnal Sisfokom data mining machine learning customer retention |
| title | Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning |
| title_full | Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning |
| title_fullStr | Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning |
| title_full_unstemmed | Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning |
| title_short | Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning |
| title_sort | prediksi customer retention perusahaan asuransi menggunakan machine learning |
| topic | data mining machine learning customer retention |
| url | http://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/1507 |
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