客户忠诚度建模集使用了一个带有LRIFMQ参数的k - memeq算法

Aloysius Matz Teguh Utomo
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引用次数: 1

摘要

忠诚的客户是决定企业发展的因素之一。因此,企业需要一种策略来保持客户的忠诚度,甚至让以前不那么忠诚的客户变得更加忠诚。所使用的策略必须根据客户细分目标正确。本文的目的是建立一个客户忠诚度集群模型,以帮助企业做出正确的营销策略决策。使用k-means算法以LRIFMQ(长度、最近度、间隔、频率、货币、数量)为参数进行分割,并计算每个集群的CLV(客户生命周期价值)。从PT. XYZ(一家从事食品加工的公司)获得的数据为一年(2019年1月1日至2019年12月31日),有337.739笔交易,26.683名客户。由于该方法具有一致性指标的计算,因此采用AHP(层次分析法)方法对LRIFMQ进行加权。利用剪影系数计算聚类质量,确定最优聚类数量。当剪影系数值为0,632904,聚类数为6时,效果最佳。
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Pemodelan Cluster Loyalitas Customer Menggunakan Algoritma K-Means Dengan Parameter LRIFMQ
Loyal customers are one of the factors that determine the development of a business. Therefore, businesses need a strategy to keep customers loyal, even making customers who were previously less loyal to become more loyal. The strategy used must be right on target according to customer segmentation. The purpose of this paper is to model a cluster of customer loyalty to help businesses in making the right decisions of marketing strategy. Segmentation is done using the k-means algorithm with LRIFMQ (length, recency, interval, frequency, monetary, quantity) as parameters, and the CLV (customer lifetime value) of each cluster is calculated. Data obtained from PT. XYZ (a company engaged in food processing) for one year (1 January 2019 - 31 December 2019), with 337.739 transactions, and 26.683 customers. AHP (analytical hierarchy process) method is used for LRIFMQ weighting because this method has a consistency index calculation. The silhouette coefficient is used to calculate the cluster quality and determine the optimal number of clusters. The best results are obtained with the silhouette coefficient value of 0,632904 with the number of clusters 6.
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