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Building customer segments using Principle Component Analysis (PCA)

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.


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Comment by Mark Pundurs on October 2, 2013 at 1:59pm

I've never before seen PCA presented as a clustering technique. It seems to me that at least in principle cluster boundaries can be oriented in any manner with respect to the various component axes. What's the connection between PCA and clustering?

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