A COMBINATION OF K-MEANS AND DBSCAN CUSTOMER SEGMENTATION IN B2B BUSINESS: A CASE STUDY IN ELECTRICAL AND MECHANICAL PARTS INDUSTRIES
Issued Date
2022-11-01
Resource Type
ISSN
21852766
Scopus ID
2-s2.0-85139505551
Journal Title
ICIC Express Letters, Part B: Applications
Volume
13
Issue
11
Start Page
1133
End Page
1141
Rights Holder(s)
SCOPUS
Bibliographic Citation
ICIC Express Letters, Part B: Applications Vol.13 No.11 (2022) , 1133-1141
Suggested Citation
Asavaphongsavanich V., Thammaboosadee S., Chuckpaiwong R. A COMBINATION OF K-MEANS AND DBSCAN CUSTOMER SEGMENTATION IN B2B BUSINESS: A CASE STUDY IN ELECTRICAL AND MECHANICAL PARTS INDUSTRIES. ICIC Express Letters, Part B: Applications Vol.13 No.11 (2022) , 1133-1141. 1141. doi:10.24507/icicelb.13.11.1133 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84244
Title
A COMBINATION OF K-MEANS AND DBSCAN CUSTOMER SEGMENTATION IN B2B BUSINESS: A CASE STUDY IN ELECTRICAL AND MECHANICAL PARTS INDUSTRIES
Author's Affiliation
Other Contributor(s)
Abstract
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.