Cluster segmentation is a descriptive, multivariate technique that creates distinct, homogeneous groups within your customer base.  The goal of cluster segmentation is to classify consumers or businesses based on behaviors, demographics or firmographics, and/or attitudes.  In this way, you can develop more targeted programs and tailor messages based on the needs and characteristics of specific groups.  One client reorganized their marketing department as a result of a segmentation project I worked on, assigning one marketer to each segment so that consistent messaging and product offers could be employed against each customer group.  Further,the segments that are developed can be combined with models or other segmentation schemes to identify the best customers to target for particular campaign or offer. 

Determining what methodology to use for clustering depends on many factors including your clustering software, the type of data you have, and the number of consumers or businesses available for segmentation.  You should also consider the optimal number of segments to meet the business objective and which behaviors or other factors are most important in defining customers.    

Regardless the methodology chosen, you will need to do data prep.  You typically start with data summarized to the household level for B2C analysis and establishment or enterprise level for B2B analysis.  You might also need to do missing value substitution, transform categorical variables to binary or scaled variables, weight variables to drive preferred ones into the solution,  and standardize continuous variables.

Data reduction might also be necessary if you have many variables.  Tools for data reduction include correlation analysis, principal components and factor analysis.  

Once that is complete, you can create your segmentation schemes.  I run many more segmentation solutions than I show to a client because I want segments that are actionable within the client’s marketing plans and that are intuitive as well as not overly complicated.  In addition, I test the validity of my cluster solutions through goodness of fit statistical measurements and by replicating my results on a hold-out sample.   The end result is that a company can align its marketing efforts against segments, taking a customer-centered approach rather than treating every customer the same.  Cluster segmentation can be a tool for giving the right message at the right time to the right person.

My clients often know who their best customers are.  Typically the best are the top 20% of customers that generate 80% of the profits.  These are the customers you most want to retain.  The question becomes who are the customers that you should try to migrate into your best customer segment?  Figuring out who are the next best requires research into their behaviors, demographics or firmographics, and attitudes. 

 Segmentation is one way to separate your customer base into differentiated groups against which relevant marketing communicationsand strategies can be developed and executed.  There are many different types of segmentation and techniques including cluster analysis, RFM and CHAID.

Regardless of what method you choose, bear in mind that a good segmentation scheme is often a result of art and science.  Segments should make sense intuitively and, if they are data driven, should be sound statistically.  In my next post I will describe clustering and how that is used for segmentation.

I ran a 5K race recently and so I have been thinking about my pre-race routine.  I may not have a lucky rabbit’s foot but the morning of a race I have a ritual of sorts.  I eat my usual breakfast and of course drink coffee.  There are some things I just can’t do without!  I wear the same clothes and sneakers for the race as I wore training.  I will not do anything new or different.   

This ritual keeps me from being distracted so that I can concentrate on the race.  In this case, my ritual helps me.  But in the office, rituals can be limiting.  Always doing something the same way can get old and stale.  A colleague asked me about identifying best customers.  My first thought was an RFM or RAD segmentation because I was in the midst of a RAD segmentation.  It would have been easy to stop there.  However, I couldn’t stop until I also suggested clusters and CHAID.   If she had let me, I would have added modeling and NPV.   The trick is knowing when to stick with rituals and when to avoid them.