Patent Segmentation Based on Customer Lifetime Value Analysis Using Regency, Frequency, Monetery, and Interpurchase Time Models

Mohamad Shodikin, Chastine Fatichah, Nungky Taniasari

Abstract


Understanding the characteristics of inpatients and outpatients based on demographic status, financial status and preferences for types of services is necessary to determine appropriate marketing strategies in order to maintain patient visits. This research focuses on the history of inpatient and outpatient medical services for patient segmentation. This research proposes Customer Lifetime Value (CLV) analysis using the Recency, Frequency, Monetary and Interpurchase Time (RFMT) model for patient segmentation. The research stages include calculating the RFMT score, clustering using the K-Means and DBSCAN algorithms and patient segmentation based on CLV analysis. The dataset in this study was obtained from inpatient and outpatient visits at a hospital from January-December 2022. The research results show that the most optimal clustering uses the DBSCAN algorithm for both inpatient and outpatient service datasets. The best Silhouette Index (SI) value for clustering the inpatient dataset is 0.99 with an Eps value of 0.8 and a MinPts value of 8. The best SI value for clustering the outpatient dataset is 0.91 with an Eps value of 0.9 and a MinPts value of 75. The application of CLV in this research is based on RFMT values which are weighted using the Fuzzy Analytical Hierarchy Process (Fuzzy AHP) technique. The results of research on inpatient and outpatient services produce different AHP values because they have RFMT priority levels. This research resulted in four segmentations of outpatients and four inpatients. The inpatient segment consists of Champions (3%), Loyal Customers (7%), Potential Loyalists (16%) and Lost Customers (75%). Meanwhile, the outpatient patient segment consists of Champions (8%), Loyal Customers (13%), Potential Loyalists (25%) and Lost Customers (54%).

Keywords


Patient Segmentation, RFMT, Clusterization , CLV, Fuzzy AHP

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References


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DOI: http://dx.doi.org/10.12962/j20882033.v35i1.20162

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