Open AccessClustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors
Author(s) -
Amir Mahmud Husein,
Februari Kurnia waruwu,
Yacobus M.T. Batu Bara,
Meleyaki Donpril,
Mawaddah Harahap
Publication year - 2021
Publication title -
sinkron
Language(s) - English
Resource type - Journals
eISSN - 2541-2019
pISSN - 2541-044X
DOI - 10.33395/sinkron.v6i1.11191
Subject(s) - cluster analysis , market segmentation , computer science , profitability index , segmentation , direct marketing , marketing , k means clustering , data mining , business , machine learning , artificial intelligence , finance
Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.