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.