Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

Ghea Sekar Palupi, Muhammad Noor Fakhruzzaman

Abstract


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.  

Keywords


Ant K-means; Logistics; Machine learning; Retailer segmentation; Sustainable industry

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DOI: http://doi.org/10.11591/ijece.v12i6.pp6132-6139

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578