Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering

Abstract
Packaging has a significant impact on how consumers perceive items and how they are identified and assessed. In the confectionery sector, customers may interpret the same product differently depending on the content and visual components on the package. The purpose of this study is to define customer categories based on perceptual criteria linked to packaging and assess consumer perceptions of a chocolate candy product. The K-Prototypes clustering approach, which works well with mixed-type data that includes both numerical and categorical variables, was used in the study. Perception with packaging, perception without packaging, perception confidence level, packaging-reading habits, and perceived packaging influence were among the factors examined. Data pretreatment, including data cleaning, feature selection, and normalization, was done before clustering. The clustering evaluation produced a Silhouette Score of 0.155, and a Davies-Bouldin Index of 2.194. Although the obtained cluster separation was moderate, the clustering results successfully revealed meaningful consumer perception segments with distinct characteristics related to packaging influence and product recognition. Three different consumer categories were identified by the results: Uncertain Perception (Non-Packaging Readers), Correct Perception (Candy), and Incorrect Perception (Chocolate). The results show that consumer disparities in product recognition are mostly influenced by packaging. Customers' categorization and identification of the product were shown to be influenced by differences in package perception, confidence level, and packaging-reading behavior. These findings show that packaging is an efficient communication tool that influences customer perception in addition to acting as a protective component. Based on consumer perception characteristics, the study offers useful insights for enhancing container design and creating more successful marketing communication tactics.
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How to Cite

Ramadhani, et al. (2026). Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering. Big Data Analytics and Data Science, 1(2). https://doi.org/10.66472/bdas.v1i2.504

Ramadhani, Dafinah; Zahwa, Aniq Farichatus, "Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering," Big Data Analytics and Data Science, vol. 1, no. 2, 2026.

Ramadhani, Dafinah; Zahwa, Aniq Farichatus. "Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering." Big Data Analytics and Data Science, vol. 1, no. 2, 2026.

Ramadhani, Dafinah; Zahwa, Aniq Farichatus. "Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering." Big Data Analytics and Data Science 1, no. 2 (2026).

Ramadhani, et al. (2026) 'Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering', Big Data Analytics and Data Science, 1(2). doi: 10.66472/bdas.v1i2.504.

Ramadhani, Dafinah; Zahwa, Aniq Farichatus. Customer Perception Analysis of Chocolate Candy Products Through Packaging Influence Using K-Prototypes Clustering. Big Data Analytics and Data Science. 2026;1(2).

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