A Sample Strategic Marketing Application: Patient Segmentation And Channel Analysis With The LRM Model

Abstract

This research aims to develop a new customer segmentation method and to propose strategies for acquiring new customers accordingly. To this end, data from 48,870 patients of a healthcare institution were segmented using the K-Means method. Patients were classified based on their longevity (L), recency (R), and monetary return (M) status and analyzed according to acquisition channels. The findings revealed that the total patients were divided into four distinct clusters. Two clusters containing 2,981 patients, representing 6% of the total, were identified as the ideal segments. While evaluating the clusters, a new indicator based on the Profitability Ratio per Patient was also utilized. The research concluded that the hospital primarily acquires patients through referral channels, with search engines and the website as the second most effective channel. At the same time, social media advertising had a comparatively lesser impact on patient acquisition. Furthermore, it was found that there were no significant differences among customer acquisition channels between the clusters. Recommendations for managers at the end of the study include maintaining more comprehensive customer data, developing profiles for cluster patients for similar sales activities, organizing "refer a friend" campaigns, and conducting evaluations between channel costs and profits.

Keywords

• segmentation
• LRFM model
• K-means method
• clustering
• strategic marketing

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