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.

If there is a silver lining to this recession for marketers, it may be the focus on analysis and measurable results.  With every marketing dollar being scrutinized and questions being asked about return on marketing investment, every  tactic is being reevaluated.  For a long time, I have questioned the value of web banners.  They are easy to ignore and, as a result, have lower response rates than other marketing vehicles.  Advocates justify the low response rates by pointing to their relative low cost.  Others say that rich media will breath life into banner ads but I remain unconvinced.

Recent articles make me think I am not the only one.  Mike Shields of Mediaweek wrote about display ads a few weeks ago.  He quoted Greg March of Wieden + Kennedy as saying “Advertisers want to deliver impact, and I don’t think the impact for these ads is always that strong.”  Shields wrote that “click-through rates for banner [ads] rarely approach 1 percent”.  I have seen much smaller rates than that.

A recent BtoB special report on 2009 marketing plans, found that 30.6% of B2B marketers surveyed were planning on increasing their spending on banners.  This sounds promising except that other online tactics had higher growth percentages:  email 68.3%, search 50.0%, web-casting 42.9%, web site development 66.3%, and social media 46.6%. 

With new tools, analysts may be able to measure the impact of online ad campaigns, taking into account every ad served up to the user regardless of whether or not she clicks on it.  Hopefully we will soon be able to answer the question, what is the value of a banner ad?