TY - JOUR
T1 - Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm
AU - Palupi, Ghea Sekar
AU - Fakhruzzaman, Muhammad Noor
N1 - Publisher Copyright:
© 2022 Institute of Advanced Engineering and Science. All rights reserved.
PY - 2022/12
Y1 - 2022/12
N2 - We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters' customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem.
AB - We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters' customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem.
KW - Ant K-means
KW - Logistics
KW - Machine learning
KW - Retailer segmentation
KW - Sustainable industry
UR - http://www.scopus.com/inward/record.url?scp=85139073795&partnerID=8YFLogxK
U2 - 10.11591/ijece.v12i6.pp6132-6139
DO - 10.11591/ijece.v12i6.pp6132-6139
M3 - Article
AN - SCOPUS:85139073795
SN - 2088-8708
VL - 12
SP - 6132
EP - 6139
JO - International Journal of Electrical and Computer Engineering
JF - International Journal of Electrical and Computer Engineering
IS - 6
ER -