Patient Segmentation Based On Customer Lifetime Value Analysis Using Recency, Frequency, Monetary And Interpurchase Time Models

Abstract

Hospitals strategically provide quality health services to the surrounding community. One of the strategic roles is realized in inpatient and outpatient services. Based on the background above, the author examines patient segmentation based on Customer Lifetime Value analysis using the RFMT model. 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 to December 2022. The research results show four segmentations of outpatients and four inpatients based on CLV values: Champions, Loyal Customers, Potential Loyalists, and Lost Customers. Inpatient segmentation includes the Champion category, 473 (2%) patients belonging to 14 clusters (15.56%). The Loyal Customer Category is 1,727 (7%) patients who are members of 31 clusters (34.44%). The Potential Loyalist category is 3,874 (16%) patients who are members of 31 clusters (34.44%). The Lost Customer Category was 18,516 (75%) patients from 14 clusters (15.56%). Outpatient segmentation includes: Champion category, 3,512 (8%) patients belonging to 10 clusters (10.10%). The Loyal Customer Category is 5,661 (13%) patients who are members of 24 clusters (24.24%). The Potential Loyalist category is 11,070 (25%) patients who are members of 34 clusters (34.34%). In the Lost Customer Category, 23,678 (54%) patients belong to 31 clusters (31.31%).