A COMBINATION OF K-MEANS AND DBSCAN CUSTOMER SEGMENTATION IN B2B BUSINESS: A CASE STUDY IN ELECTRICAL AND MECHANICAL PARTS INDUSTRIES

• Main Author: Asavaphongsavanich V.
• Other Authors: Mahidol University
• Format: Article
• Published: 2023
• Subjects: Computer Science
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/84244

An industry sector is important for the economic growth in Thailand. Among those industries, the electrical and mechanical parts manufacturers are also essential to drive the production process in the factory. Due to the foundation activity supporting, the industrial part manufacturer has become more competitive. The business report in 2019 stated the lost customers and open status of quotations are increasing dramatically. In order to solve and further prevent these problems and gain more competitive advantage, the data mining technique would be necessary to descriptively understand and predict customer behavior which can improve the business strategy to be more effective, which the return-of-investment of the simulated business scenario will prove. The data used in this paper is customer data between 2017 and 2020 in two entities: 1) customer char-acteristic data, including registered capital, industry code, business type, business size value, and 2) customer transaction data, including purchase history. The combination of descriptive segmentation and predictive modeling toward decision-making strategies that tend to increase the return-of-investment of the industries is challenging, and the main contribution is specified in electrical and mechanical parts manufacturing. The ex-pected results should support the Sales and Marketing team in increasing sales value and new customers and maintaining existing customers by offering highly accurate strategy segmentation.