Market segmentation typically involves forming groups of similar people. The characteristics of people that are used to determine if the people are similar are called segmentation variables. For example, if segmenting a market is based on the age of people, then age is the segmentation variable.

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When people refer to segmentation variables, they are usually referring to one of the following:

  • A single variable that is used to allocate people to segments
  • A set of variables where people are used to allocate people to segments based on some logical relationship
  • A set of variables that are used in a predictive statistical algorithm to predict segmentation membership
  • A set of variables that are used in a segmentation algorithm
  • A variable in a data file or database that records segment membership

A single variable that is used to allocate people to segments

Often, one key characteristic of people is used to define segments. Airlines allocate flyers into segments (tiers) based on status credits (longer flights and more expensive flights earn more status credits). Banks allocate customers into segments based on the profit that the customers are likely to provide to the bank.

A set of variables used to allocate people to segments based on a logical relationship

Sometimes there are logical relationships between small numbers of variables that can be exploited when allocating customers to segments. Direct marketers allocate customers into segments and prioritize these segments based on how recently people have responded, how frequently they have responded, and how much they have spent when previously buying things as a result of direct-mail campaigns. Packaged-goods companies like Nestle and Unilever classify people into life-stage segments, based on the age, marital status, and the number of children (e.g., one segment of families with young children, another with teens, “empty nesters”, etc.).

A set of variables that are used in a predictive statistical algorithm to predict segmentation membership

Sometimes a single variable has been identified as being useful for segmentation, but the variable’s value is unknown for some people. For example, a bank may know how much profit its customers provide, but the bank cannot know the potential profit of competitors’ customers. Predictive models can be used to predict the profit of the current customers based on other variables, such as age, where people live, marital status, race, etc. These other variables are sometimes referred to in this context as segmentation variables, and the predictions made using these segmentation variables can be used to prioritize the customers of other banks (e.g., to target with Facebook ads).

A set of variables that are used in a segmentation algorithm

Sometimes a large number of variables are identified, and segmentation algorithms, such as k-means cluster analysis and latent class analysis, are used to identify groups of people that are similar to each other. The variables that are used in this analysis are referred to as segmentation variables.

A variable in a data file or database that records segment membership

When segments are formed using any of the methods discussed above, people in a database or data file are assigned into segments. E.g., the first person may be in segment 1, the second person in segment 3, and so on. The data that contains this segment membership information is also often referred to as the segmentation variable.

This article refers/restricts itself to people. However, the same ideas apply to other units of analysis (e.g., grouping households, countries, occasions, etc.).

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