TY - GEN
T1 - Segmenting and Targeting the Potential Markets of a Muslim Fashion Company
AU - Dhini, Arian
AU - Budiani, Larastika Rahmadanty
AU - Laoh, Enrico
N1 - Funding Information:
The authors would like to express the gratitude and appreciation to the Directorate of Research and Development - Universitas Indonesia, for funding the study through the PUTI prosiding grant, with contract number NKB- 1070/UN2.RST/HKP.05.00/2020.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/19
Y1 - 2020/11/19
N2 - The increasing use of technology in the big data world enables companies to use consumers' information, which leads to increased competition in the muslim fashion industry in Indonesia. Muslim fashion business brands need the correct strategy to gain the market. The combination of marketing and data mining is translating consumers' data into marketing strategies. This study selects a muslim fashion company in Indonesia to explore the market segment and target the segment to determine the marketing strategy. The segmentation is based on Length, Recency, Frequency, and Monetary (LFRM) variable. It applies K-means algorithm to cluster consumers and Davies Bouldin (DB) validation index to determine the best K value. Once the cluster is established, the analysis is conducted to find potential customer segments. The main target of the company marketing activities is those potential segments. The study grouped into five clusters of customers. From those five clusters, two clusters are selected as market targets for applying marketing activities.
AB - The increasing use of technology in the big data world enables companies to use consumers' information, which leads to increased competition in the muslim fashion industry in Indonesia. Muslim fashion business brands need the correct strategy to gain the market. The combination of marketing and data mining is translating consumers' data into marketing strategies. This study selects a muslim fashion company in Indonesia to explore the market segment and target the segment to determine the marketing strategy. The segmentation is based on Length, Recency, Frequency, and Monetary (LFRM) variable. It applies K-means algorithm to cluster consumers and Davies Bouldin (DB) validation index to determine the best K value. Once the cluster is established, the analysis is conducted to find potential customer segments. The main target of the company marketing activities is those potential segments. The study grouped into five clusters of customers. From those five clusters, two clusters are selected as market targets for applying marketing activities.
KW - Customer segmentation
KW - DB Index
KW - K-means algorithm
KW - LRFM Model
KW - targeting market
UR - http://www.scopus.com/inward/record.url?scp=85099780485&partnerID=8YFLogxK
U2 - 10.1109/ICISS50791.2020.9307604
DO - 10.1109/ICISS50791.2020.9307604
M3 - Conference contribution
AN - SCOPUS:85099780485
T3 - 7th International Conference on ICT for Smart Society: AIoT for Smart Society, ICISS 2020 - Proceeding
BT - 7th International Conference on ICT for Smart Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on ICT for Smart Society, ICISS 2020
Y2 - 19 November 2020 through 20 November 2020
ER -