MAPPING OF POTENTIAL CUSTOMERS AS A CLOTHING PROMOTION STRATEGY USING K-MEANS CLUSTERING ALGORITHM
Abstract
The high demand for clothes causes the development of the clothing industry in Indonesia continues to increase. Increasing the number of competitors among apparel traders is also unavoidable. This is also experienced by clothing traders with an online concept. Therefore the right sales strategy is needed to be able to survive or even win the competition. One thing that can be done is to apply technology to promote to obtain and maintain potential customers. However, promotions that are carried out without a clear and measurable concept can cause harm if carried out on target. The same thing happened in the Mustika Gerai online clothing store which was the location of observation, so far the concept of promotion was carried out by lowering prices and discounts for all customers. As a result, what happens is that sales turnover decreases dramatically while new customers who expect it may not necessarily be achieved. The purpose of this study is to research by applying data mining technology in the Gerai Mustika customer data warehouse to map potential customers as targeted promotional strategies. The data mining technique used is the k-Means Clustering method. The process of extracting information in the form of pattern discovery/mapping is then integrated using the Rapidminer software. From the results of the analysis that has been done, it can be concluded that the application of the k-means method can map potential customers based on regions or sub-districts, namely cluster 1 has 3 districts, cluster 2 has 7 districts and cluster 3 has 13 districts. These results are strengthened by RapidMiner software testing with data accuracy following the results of calculations from 23 data
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