Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

Abstract

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.

Predictive Analytics in Marketing: Contribution to Marketing Performance

“…Market segmentation, when coupled with predictive analytics, has been found to positively impact the productivity and upgrading of enterprises, demonstrating the importance of tailored strategies based on segmented customer data (Yuan & Pan, 2022). Furthermore, the application of clustering algorithms for marketing targets based on customer purchase patterns and behaviors has become crucial, especially in the digital shopping era, emphasizing the need to predict customer behavior accurately for effective marketing strategies (Husein et al, 2021).…”