Segmentation of Mentoring Customer Characteristics Using the K-Means Method and Hierarchical Clustering for Customer Relationship Management (CRM)

Hanif Aristyo Rahadiyan

Abstract


In the next 10-20 years, it is expected that Indonesia will enter a demographic bonus era, where the population of productive age exceeds that of non-productive age. This presents an opportunity for startups in the field of education to prepare better human resources in Indonesia. With the recent Covid-19 pandemic, the government has implemented regulations that require online teaching and learning. Startups, such as Outstanding Youth Indonesia (OYI), play a role in bridging distance learning, leading to increased competition in the education sector. To stay competitive, OYI is implementing a customer relationship management (CRM) strategy, using consumer characteristic segmentation through the K-means method and hierarchical clustering. The study aims to test the consumer characteristic cluster results and provide CRM recommendations based on the segmentation results. The results of the study revealed that the K-Means method was more optimal, with a score of 0.657, compared to hierarchical clustering of 0.644. The clusters tested included categories, intended education, and types of scholarships. Three clusters were produced: cluster 1, dominated by high school/vocational high school students; cluster 2, mostly university students; and cluster 3, dominated by employees of government agencies. Cluster one had the largest silhouette coefficient. Based on the clustering, a strategy was generated for each cluster to improve CRM in OYI.

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References


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DOI: http://dx.doi.org/10.24014/coreit.v9i1.21567

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