Classification of potential customers using C4.5 and k-means algorithms to determine customer service priorities to maintain loyalty

Abstract

The increasing competition among Middle-Class Micro Enterprises (MSMEs) is a problem because business actors must improve techniques and strategies to maintain customer satisfaction, and the number of customers continues to increase. Customers are an essential asset for the company. To maintain customer loyalty with promising prospects for the company, a strategy is needed to support this. Strategies such as service prioritization can be used to maintain customer loyalty. This research was conducted to classify customers who are estimated to have good prospects for the company so that service priorities are not mistargeted by utilizing 1683 data from store By.SIRR, a fashion store in Semarang, Indonesia contains five attributes, and customers are classified and are estimated to have promising prospects for the company. Data mining methods use the C4.5 and K-Means algorithms to classify the classification process. The research resulted in the grouping of customers into four categories: potential lover, flirting, faithful lover, and spiritual friend. From the validation test conducted using the Confusion Matrix Validation method, the classification results get an Accuracy of 97.70%.