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The easy bit of market segmentation is creating segments. The hard bit is working out if the resulting segments are useful. In this post, I…

This post describes how to apply the three standard ways of scaling (transforming) rating scales data prior to using cluster analysis and other algorithms for…

When segmenting a market, a practical challenge is to work out the number of segments. There are eight approaches to choosing the number of segments.

Cluster analysis techniques require users to specify the number of clusters that they require. Six classes of approaches to choosing the number of clusters:

Market segmentation typically involves forming groups of similar people. Segmentation variables are characteristics used to determine if they are similar.

Latent class analysis requires the analyst to specify the number of classes prior to the application of the technique. Learn more about the seven approaches

A practical challenge when working out how to segment is that there are usually lots of possible variables, and you need to reduce that number.

Golden questions are used to allocate people to segments. They are also known as self-selection questions. The main applications of golden questions are:

This article discusses how to work out which segmentation variables are appropriate from a list of variables.

This page lists the key frameworks and processes for identifying relevant variables to use when segmenting a market. The best way to identify relevant

Guide to Operational Segmentation and Inspirational Segmentation. Learn more in this informative article by data scientist Tim Bock on displayr.com

Market segmentation research is research that is used to help a firm identify segments in a market, with the end goal of developing different strategies.

Market segmentation involves splitting a market into segments and developing different tactics and strategies for the segments.

A dendrogram is a diagram that shows the hierarchical relationship between objects, often created as an output from hierarchical clustering. Read more.