Customer Segmentation with RFM Model using Fuzzy C-Means and Genetic Programming

Anas Syaifudin · Purwanto Purwanto · Heribertus Himawan · Moch Arief Soeleman

5 citations0/0 files storedMatrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer

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Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

One of the strategies a company uses to retain its customers is Customer Relationship Management (CRM). CRM manages interactions and supports business strategies to build mutually beneficial relationships between companies and customers. The utilization of information technology, such as data mining used to manage the data, is critical in order to be able to find out patterns made by customers when processing transactions. Clustering techniques are possible in data mining to find out the patterns generated from customer transaction data. Fuzzy C-Means (FCM) is one of the best-known and most widely used fuzzy grouping methods. The iteration process is carried out to determine which data is in the right cluster based on the objective function. The local minimum is the condition where the resulting value is not the lowest value from the solution set. This research aims to solve the minimum local problem in the FCM algorithm using Genetic Programming (GP), which is one of the evolution-based algorithms to produce better data clusters. The result of the research is to compare the application of fuzzy c-means (FCM) and genetic programming fuzzy c-means (GP-FCM) for customer segmentation applied to the Cahaya Estetika clinic dataset. The test results of the GP-FCM yielded an objective function of 20.3091, while for the FCM algorithm, it was 32.44741. Furthermore, evaluating cluster validity using Partition Coefficient (PC), Classification Entropy (CE), and Silhouette Index proves that the results of cluster quality from gp-fcm are more optimal than fcm. The results of this study indicate that the application of genetic programming in the fuzzy c-means algorithm produces more optimal cluster quality than the fuzzy c-means algorithm.

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2018The impact of the customer relationship management on the organization performance 2012Customer Relationship Management 2021Customer relationship management and its impact on innovation: A literature review 2021RFM-based repurchase behavior for customer classification and segmentation 2018Approaches to Clustering in Customer Segmentation 2019Estimating Customer Segmentation based on Customer Lifetime Value Using Two-Stage Clustering Method 2020PENERAPAN FUZZY C-MEANS KLUSTER UNTUK SEGMENTASI PELANGGAN E-COMMERCE DENGAN METODE RECENCY FREQUENCY MONETARY (RFM)2019Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

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