SEGMENTATION MODEL OF CUSTOMER LIFETIME VALUE USING K-MEANS ALGORITHM

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dc.contributor.authorMurnawan
dc.date.accessioned2021-02-18T08:29:24Z
dc.date.available2021-02-18T08:29:24Z
dc.date.issued2020
dc.description.abstractThis research aims to produce Customer Lifetime Value (CLV) values for each customer segment using the LRFM method (length, recency, frequency, monetary) and in clustering using the K-Means algorithm. The clusters produced in this research were 3 clusters. The results of the three segments have been tested for performance using Euclidean distance. The CLV value will be generated by multiplying the LRFM normalization value by the LRFM weight value and then adding it up. The sum of the CLV values is carried out for each cluster that has been formed. The percentage of the number of members in segment 1 is 50% with a CLV value of 0.3201328, segment 2 is 7% with a CLV value of 0.4646494 and segment 3 is 43% with a CLV value of 0.2311797. Analysis based on the LRFM value of each segment shows that segment 2 is the segment that has the best CLV value. This final project produces a visualization of Shiko Outdoor UMKM customer segmentation with interactive graphics and images in the form of a web-based dashboard.en_US
dc.identifier.issn0038-111X
dc.identifier.urihttp://repository.widyatama.ac.id/xmlui/handle/123456789/12651
dc.language.isoenen_US
dc.publisherSolid State Technology Volume: 63 Issue: 3en_US
dc.subjectCustomer Segmentationen_US
dc.subjectCustomer Lifetime Valueen_US
dc.subjectEuclidean Distanceen_US
dc.subjectLRFM modelen_US
dc.subjectK-Meansen_US
dc.titleSEGMENTATION MODEL OF CUSTOMER LIFETIME VALUE USING K-MEANS ALGORITHMen_US
dc.typeArticleen_US
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