Customer Segmentation Using K-Means Clustering

We used several exploratory data analytics techniques and different types of data visualizations such as pair plots, distribution plots, box plots, correlation heatmap and scatter plots. From the results of K-means clustering model we found 3 different demographic and geographic group of customers. Each of these groups consists of customers from different locations, gender and age. First group consists of mostly from married women who lives in small cities. Second group majority is single men in their mid-30s living in big cities and working in skilled positions. Third group is the oldest group with average age of 55. Gender in this group evenly distributed by married women and men. This group also is highly educated group. Considering these each of them can be useful for different type of product advertisements.

Customer's occupation plays important role in peoples purchasing habits. According to JingWu:ZhengLin customers have different consumption customs in different industries. Also, Gasiorowska reported that men and women differ in terms of shopping behaviour. Considering these companies should establish different marketing strategies for different targeted groups.