data mining; machine learning; big data; customer segment; k-Mean algorithm; sklearn; extrapolation
The emergence of many competitors and entrepreneurs has caused a lot of tension among competing businesses to find new buyers and keep the old ones. As a result of the predecessor, the need for exceptional customer service becomes appropriate regardless of the size of the business. Furthermore, the ability of any business to understand the needs of each of its customers will provide greater customer support in providing targeted customer services and developing customized customer service plans. This understanding is possible through structured customer service. Each segment has customers who share the same market features. Big data ideas and machine learning have promoted greater acceptance of automated customer segmentation approaches in favor of traditional market analytics that often do not work when the customer base is very large. In this paper, the k-means clustering algorithm is used for this purpose. The Sklearn liabrary was developed for the k-Means algorithm (found in the Appendix) and the program is trained using a 100-pattern two-factor dataset derived from the retail trade. Characteristics of average number of customer purchases and average number of monthly customers.