Utilizing Data Mining Techniques to Analysis Changes in Purchase Behavior of Batik’s Customers

Abstract

Sales transaction data contains rich information potentially used to support company competitiveness. However, interpreting and utilizing transaction data in developing marketing strategies remains a challenge, even for big companies. Therefore, this research aims to develop marketing strategies using data mining techniques. A medium-sized company focusing on producing and selling traditional motif clothes (batik) will be used as a case study. The negative sales trend is the biggest issue currently faced by the company. Hypothetically, this problem is caused by imported products sold at lower prices or changing consumer behavior after pandemic covid. Currently, the company only implements simple analysis of its transaction data. The analysis of transaction data, conducted through five data mining stages, yielded a shift from purchasing small quantities to larger quantities, increased purchases during the final week of each month, and increased purchases on religious occasions. Furthermore, the analysis revealed that 31.29% of all transactions were attributed to loyal consumers, and 192 customers exhibited in Cluster 1 (high transaction quantities and high transaction values). Further investigation also revealed that customers categorized as loyal customers and Cluster 1 have different behaviors that can be used to develop further customer relationship programs. Future research can be conducted by employing data mining techniques to study the organization's assortment of products. Management discussions reveal that changes in consumer buying behavior extend to the selection of items and batik themes.