Coffee Buyer Segmentation using PCA Model and K-Means Algorithm on Coffee Customers
DOI:
https://doi.org/10.33050/icit.v12i02.4330Keywords:
Coffee, PCA, K-Means, Customer SegmentationAbstract
Coffee is a plant that originates from the agricultural industry which is produced and made into a drink through coffee beans and a series of processes such as roasting and grinding it into coffee powder which will be brewed. Currently, many countries have cultivated coffee plants and there are two generally well-known coffees, namely Robusta coffee and Arabica coffee. Each buyer has different characteristics in enjoying coffee drinks, including the time of purchase, the product purchased from the coffee shop, and the location of the coffee shop. Therefore, research is needed to develop a marketing strategy that suits the needs of the buyer segment. To support this research the author uses the PCA (Principal Component Analysis) model and the K-Means method to group customer characteristics. Standard deviation is used to determine the average value of each cluster in coffee shop outlets, with a PCA value on product_id of 0.99986001 and unit_price of 0.01673209 from a total of 96% of clusters. This research can be used as a good reference for students, researchers, and can also be used as a marketing strategy medium.
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