TY - GEN
T1 - Estimating customer segmentation based on customer lifetime value using two-stage clustering method
AU - Pramono, Pradnya Paramita
AU - Surjandari, Isti
AU - Laoh, Enrico
N1 - Funding Information:
ACKNOWLEDGMENT Authors would like to express gratitude and appreciation to Universitas Indonesia for funding this study through PIT- 9 Research Grants Universitas 0061/UN2.R3.1/HKP.05.00/2019.
Funding Information:
Authors would like to express gratitude and appreciation to Universitas Indonesia for funding this study through PIT-9 Research Grants Universitas Indonesia No: NKB-0061/UN2.R3.1/HKP.05.00/2019
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - In order to cope with the competitive environment related to beauty industry sector in Indonesia, companies need to manage and evaluate customer interactions by enhancing Customer Relationship Management (CRM). This study aims to specify customer segment that has similar lifetime value with clustering method, hence company can conduct appropriate strategies to the right segment. Two-stage clustering method for segmenting customers is proposed in this study. Ward's method is used for choosing an initial number of cluster and K-Means method to perform clustering analysis. Two approaches using LRFM (Length, Recency, Frequency, Monetary) model and extended model called LRFM - Average Item (AI) variables in clustering process are compared by validity index to obtain the best result for customer segmentation. The result shows that adding new variable Average Item in LRFM model have no significant difference or better results in clustering. The ranking process based on Customer Lifetime Value (CLV) score is conducted using weighted LRFM model variables. Final weight score for all variables are obtained from Fuzzy AHP method. In summary, company also get several inferences such as customer characteristics of high and less potential customers. It can be a guideline for making the sale and marketing strategies.
AB - In order to cope with the competitive environment related to beauty industry sector in Indonesia, companies need to manage and evaluate customer interactions by enhancing Customer Relationship Management (CRM). This study aims to specify customer segment that has similar lifetime value with clustering method, hence company can conduct appropriate strategies to the right segment. Two-stage clustering method for segmenting customers is proposed in this study. Ward's method is used for choosing an initial number of cluster and K-Means method to perform clustering analysis. Two approaches using LRFM (Length, Recency, Frequency, Monetary) model and extended model called LRFM - Average Item (AI) variables in clustering process are compared by validity index to obtain the best result for customer segmentation. The result shows that adding new variable Average Item in LRFM model have no significant difference or better results in clustering. The ranking process based on Customer Lifetime Value (CLV) score is conducted using weighted LRFM model variables. Final weight score for all variables are obtained from Fuzzy AHP method. In summary, company also get several inferences such as customer characteristics of high and less potential customers. It can be a guideline for making the sale and marketing strategies.
KW - Clustering
KW - Customer Lifetime Value
KW - Customer Relationship Management
KW - Customer Segmentation
KW - Fuzzy AHP
UR - http://www.scopus.com/inward/record.url?scp=85074886309&partnerID=8YFLogxK
U2 - 10.1109/ICSSSM.2019.8887704
DO - 10.1109/ICSSSM.2019.8887704
M3 - Conference contribution
AN - SCOPUS:85074886309
T3 - 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019
BT - 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Service Systems and Service Management, ICSSSM 2019
Y2 - 13 July 2019 through 15 July 2019
ER -