Pengembangan Strategi Bisnis Berbasis Data dengan K means Clustering dan Business intelligence Studi Kasus Soo

K-means Clustering dan Business intelligence

Abstract

The research conducted at Soo Kyo Café aimed to develop strategies and business recommendations for Soo Kyo Café management by leveraging customer assessment data, K-means clustering, and business intelligence. This research was motivated by the emerging trend of Café and coffee shop visits and growth in Indonesia, particularly in the Soloraya region.

The research activities included problem identification, data collection (observations and questionnaires) held by management, data analysis using K-means clustering, and business intelligence implementation to identify strategic patterns.

The results of the customer data analysis using K-means Clustering revealed that Cluster 1, with an age range of ≤24 years, was highly satisfied. Cluster 2, with a range of 25–25 years, was satisfied. Cluster 3, with a range of 36 years and above, was dissatisfied with the offerings. For business intelligence purposes, Cluster 1 required menu variations tailored to the age of its members, such as drinks with trendy flavors and snacks at affordable prices. Cluster 2 requires menu improvements by adding bundled offerings, while Cluster 3 does not require a specific strategy due to the significant differences in the assessment of the facilities offered. The proposed strategies include increasing menu variety, optimizing service, digital promotions for younger customers, and implementing a loyalty program.