Estimating Customer Lifetime Value using LRFM Model in Pharmaceutical and Medical Device Distribution Company

Dealing with intense competition encourages companies to build strong customer relationship management to improve customer satisfaction and retention. Extracting information related to customer lifetime value (CLV) supports the company to treat each customer differently based on their contribution to the profitability of the company. This study aims to estimate customer lifetime value of each cluster based on length, recency, frequency, and monetary (LRFM) model, hence provides the company an insight to evaluate their customers and improve marketing strategies. The proposed methodology is a combination of clustering k-means algorithm and AHP method to calculate the CLV using LRFM model in pharmaceutical and medical device distribution company. k-means algorithm was applied to cluster customers, while AHP method was used to gain information related to the importance of LRFM model. AHP results demonstrated that frequency is the most important variable in this sector. Finally, CLV was calculated and ranked for the resulting eight optimum clusters. Cluster 3, cluster 6, and cluster 8, which are the high value loyal customers, are the core customers of the company due to their CLV value. Therefore, the company should allocate company resources to focus on profitable customers.