Customer Segmentation of Botanical Contact Printed Products Using K-Means Clustering

The growing demand for sustainable fashion has catalysed an interest in botanical contact printed products worldwide, which utilize nature to create unique imprints and textile designs. In this study, we have segmented the available data into distinct clusters using the k- means method, which is an unsupervised machine learning technique, to determine the data points that lie within the confines of this market. By analysing the data collected—including earnings and expenditures, preferences, and demographic information—the aim is to uncover meaningful patterns that can shape marketing strategies for evolving sustainability. Recognizing customer groups is essential for businesses for several reasons. In the first place, it helps companies direct marketing more precisely, increasing the possibility that within the available resources a message would be heard by the targeted industry groups. Apart from that, it is possible to optimize the range of products as a defined customer segment would appreciate the product features. Thirdly, it helps to gain loyalty because people want to be recognized with brands that resonates their belief systems. Insightful data derived from K- means clustering helps businesses increase customer satisfaction, revenue, and growth in the sustainable textile sector. Having consistent actionable marketing strategies based on data is vital to the growth of companies in the textile sector that wish to tap into the more environmentally focused customers.