TRC

By Rajan Sambandam

Segmentation studies in practical market research generally fall into two categories: those that are related to company databases and those that aren’t. The latter are often based on survey data alone and tend to primarily use attitudinal information for identifying segments in the market. Segments so formed are quite rich, but once the segments have been formed identifying segment members in the broader market is hard to do. Some form of broad targeting has to be used, perhaps based on demographic and media usage variables. On the other hand, segments formed on the basis of database variables don’t face the same problem since it is quite straightforward to score the entire database. But often the use of database variables alone does not provide sufficiently rich segment descriptions. This poses a dilemma for companies that want rich segmentation schemes and the ability to score their database using the segments so developed. Should they go with an attitudinal segmentation scheme or a demographic/transactional segmentation scheme?

Traditionally the answer has been far less than ideal. One option is to create rich segments using attitudinal variables and then try to predict those segments using whatever information is available in the database, with the hope of developing acceptable prediction equations that can then be used to score the entire database. The problem with this approach is that demographic, transactional and behavioral variables usually do not predict attitudes well, thus causing substantial misclassification. Another approach is to mix attitudinal variables with variables from the database in creating the segmentation scheme in the hopes of creating more “friendly” prediction equations. How well this works often depends on which type of variable was more influential in forming the segments, and based on that the quality of the prediction equations would vary. A third approach is to use attitudinal variables only in the creation of segments and prediction equations. Customers in the database are then scored based on the answers they give to the set of attitudinal questions. Of course, this works only in cases where a company has the means to query a large number of its customers on the key questions.

Object Based Segmentation

A more recent proposed solution to this problem is Object based segmentation. In this approach, a large number of segments are initially formed using demographic or transactional variables already available in the database. These segments are called the objects. Next these objects are used as the “respondents” in another segmentation analysis where the basis variables for the analysis are the attitudinal variables that are likely to yield rich segments. Once this analysis has been completed it becomes easier to classify the database since the objects were formed using variables already available in the database.

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