8 - Analytics 101 for the Marketeer: Clustering and Customer Behavior(s)

Clustering is the task of grouping a set of objects so that objects in the same group are closer to each other and farther away from objects in another group. Well, now that the formal definition is through, we can get down to the brass and tacks – just replace the term objects in the definition with “customers “ and we’ll be on our way.
 Consider an e-tailer who wants to understand the age –locale spread of his customers. Ideally, he would need to process age first into buckets and then bring in the locale aspect of things to see something like this.
Clustering helps make the process and visualization simple and is a standardized package in most tools. There are different types of clustering like Partitioning, Hierarchical and Density based techniques though we will be focused on Partitioning techniques like C4.5 / K means methods. This technique assumes critical importance for two reasons:

• Faceted behavioral Clustering - if you have objectivized different facets of behavior .. do check out my earlier post.
• Clustering in itself lays the foundation for employing a host of other analytical methods in managing Customer Churn and Sales Uplift aka Cross & Up Sell. Taking the previous blog’s example of first party behavior ie Purchase, Engagement and Browsing, think of all the marketing strategies – you could drive given a basic customer classification like the following matrix. Remember, the following example is pure segmentation , not clustering.
  Let’s move this a stage up and look at a clustering model that provides an algorithmic grouping of similar customers , in this case purely Purchase Behavior. The algorithm has classified customers based on their purchase propensity, into five categories High Value, Loyal, Potential, Hibernators and Vanishers.
  The analysis is for two year in store data of a retail chain depicting purchase behaviour alone. The kind of personas ( The current analysis categorizes Loyal,Potential, Nascent etc) and segments you could drive are virtually endless based on your clustering parameters that fits your business model and consumers. I will try to bring in all the behaviors we talked about in earlier  posts - Eg "Young, Valuable,Engaged, Heavy Browsing, Electronic Geek" , "Mid Aged, Potential , Slightly Disengaged, Stationery Buyer" , so on and so forth. Some level of intelligent analysis is necessary to arrive at those critical consumer parameters that drive your business. But We'll never know unless we try, would we?