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A very common approach to building and understanding customer segments is through the use of clustering techniques such as Principle Components Analysis (PCA). These clustering techniques will analyze your customer data and see if customers tend to cluster by certain features, or combinations of features. Through such an approach, a marketer can use clusters to define specific segments. For example, running a cluster analysis could end up showing two clusters: one with customers who have high values for variables related to “engagement” (e.g., emails, comments, etc.) while another could be a cluster with lower values for engagement variables, but mid-sized value for purchase-related variables (e.g., number of purchases, number of products, etc.). In this case, the marketer can conclude that two segments, “Engaged Leads”, and “Slightly Engaged Purchasers”, exist within the customer base.