This study aims to segment users of an Online Grocery Platform based on their perceptions of service quality, particularly regarding transaction security, data security, delivery timeliness, delivery speed, and shipping costs. Using the K-Means clustering method with 50 respondents, the analysis successfully formed three distinct clusters that represent different patterns of customer satisfaction. Cluster 1 consists of users who report very high satisfaction across all service attributes, particularly in transaction security, data protection, and delivery performance. Cluster 2 represents users with moderate and stable evaluations on all variables. Meanwhile, Cluster 3 includes users who express low satisfaction related to security and shipping costs but show relatively positive perceptions of delivery speed and timeliness. The segmentation illustrates that user perceptions toward the Online Grocery Platform are diverse and can be classified into clearly differentiated groups. These findings provide insights for companies in identifying priority segments and designing targeted strategies to improve service performance, especially in enhancing security aspects and optimizing shipping cost structures.
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