ANALYSIS OF SEGMENTATION AND CLIENT TARGET MARKET BUSINESS DECISIONS IN CONSTRUCTION SERVICE COMPANY USING K-MEANS AND DECISION TREE ALGORITHMS : CASE STUDY AT CV JOWON SOLUSINDO - Wondho Setia; Mardiani Mardiani (Information Technology and Science (ITScience), 2026)
High competition in the construction service industry requires companies to adopt efficient marketing strategies to reduce Customer Acquisition Costs (CAC). CV Jowon Solusindo faces challenges regarding marketing inefficiency due to the implementation of a one size fits all strategy and a high number of unconverted leads (Lost Prospects). This study aims to classify customer characteristics and discover decision rules to formulate personalized marketing strategies. This research employs a quantitative approach with Data Mining methods based on the CRISP-DM framework. The dataset consists of 576 historical transaction records that have undergone data cleaning processes. The method used is a hybrid approach, combining the K-Means Clustering algorithm for customer segmentation and the Decision Tree (C4.5) for rule pattern extraction. The results indicate that the K-Means algorithm with k=3 successfully mapped customers into three distinctive segments, Young Emerging Clients (Average age 33 years with the highest project value), Established Senior Clients (Average age 54 years with stable frequency), and Lost Prospects (Average age 42 years with the lowest offer value). The Decision Tree analysis yielded an accuracy of 67% and identified Age as the primary determinant factor with a split point at 43.5 years. Based on these findings, it is recommended to differentiate marketing strategies into digital visual approaches for customers under 43.5 years and personal approaches for those above that age, as well as pricing strategy adjustments to minimize failure in the Lost Prospects segment.