Market Segmentation

Charlie Nelson
August 2001
Most recent update January 2012




Consumer diversity is increasing rapidly and firms have long sought to differentiate their products relative to competitors.  This is where market segmentation comes in.  Long gone are the homogeneous markets that Henry Ford conquered with his mass production of one model of car (mass customisation is the new objective).  While there has been a strong move towards one-to-one marketing in recent years, market segmentation offers a valuable compromise between that holy grail and mass marketing .  Market segmentation provides a proven way of disaggregating markets in a way that can improve profitability without the investment in systems and sales resources needed for one-to-one marketing.

This document provides an overview of market segmentation and links to more detailed information sources.

Objectives of Market Segmentation

Market segmentation is the first of three important steps in developing marketing strategy.  Segmentation groups customers with similar needs and responses; targeting determines which segments to serve; positioning is about how the product (or product portfolio) should compete with others in the market.

The objectives of market segmentation are to more accurately meet the needs of selected customers in a more profitable way.

Precisely how this can be achieved will vary by company capability.  For example, a single product company may be able to boost sales and cut advertising costs if they can target consumers with a high likelihood of product purchase.  On the other hand, a company with several brands in a category will benefit by positioning each brand within the portfolio against a distinct set of consumer needs – ideally each brand should be sufficiently distinct so that there is little cannibalisation.

Segmentation Bases

There is a large array of possible segmentation bases.  Some of these are briefly described below.


Consumers can be grouped on the basis of characteristics such as age or household composition.  This is easy to do and it is easy to reach such segments with media.  But age and other demographics are only loosely related to behaviour.

Socioeconomic Characteristics

Similarly, characteristics such as income, occupation and education can be used to derive segments that are easy to reach.  Such segments are indicators (although not perfect) of behaviour such as lifestyle, price sensitivity, and brand preference.

Product Usage

Potential to use the firm’s product is a behaviourally based segmentation basis.  Potential could be determined by administering questions about disposition to use (such as awareness, used in the past, would consider using) in a survey and respondents grouped accordingly.  The problem is then how to reach the most attractive segments.  This is done either by using a large-scale single source survey (such as ACNielsen Panorama) that asks consumers about product disposition and media usage or by relating product disposition to demographics.  Both approaches are usually imperfect as behaviour is rarely strongly correlated with demographics or media usage.


Personality, attitudes, opinions, and life styles are often used a segmentation bases.  These characteristics have some relationship to behaviour and provide insight into how to communicate with chosen segments.  Reaching the chosen segments is then the issue, as discussed under product usage, above.


Generation, or cohort, refers to people born in the same period of time.  For example, the Baby Boomer generation can be defined as those people born between 1946 and 1955.  Such cohorts share much in common.  Not only are they of a similar age, but they experienced similar economic, cultural, and political influences in formative years.  Thus generation is probably a better segmentation basis than age and just as easy to reach.

Benefits Sought

Some people are price sensitive, others seek quality or service.  Some people are brand loyal, while others frequently switch brands.  It is possible to group consumers on the basis of these factors.  Note that price/quality sensitivity can vary by category.  Some people are very concerned about the quality of the food they eat but will buy cheap laundry detergent.  Others will feed themselves any rubbish but are fastidious about cleanliness.  This is a very powerful basis for segmentation but it is not easy to buy media on this basis.  These segments can be reached by the message (self-selection) but this is not necessarily cost effective. 


There are two reasons why people who live in the same area may share similar characteristics.  First, some areas have more expensive properties than others and so people with similar socioeconomic characteristics may cluster together.  Second, they have similar transport and shopping options.  It is easy to reach particular areas by using local newspapers, cinema, outdoor, and selective direct mail but mass media is less effective.


There are several commercial geodemographic segmentation schemes available, that combine demographics and geography as a segmentation basis.  This approach aims to identify groups of small geographic areas that have similar demographic profiles.  These tend to suffer from the fallacy of averages.  Some areas may be genuinely relatively homogenous but many are not and this can be very misleading.

More on geodemographic segmentation (pdf format, 1,351k).


The segmentation basis used depends on the decision to be made.  For pricing decisions, for example, the segmentation basis should be price sensitivity and deal proneness.  For advertising decisions, the bases could include benefits sought; media use; or psychographics (or some hybrid of these).

Clearly, one segmentation basis will not be ideal for all marketing decisions.  Nor will one segmentation basis be ideal for all industries – food, detergents, clothing, and motor vehicles all satisfy different needs and have different levels of purchase involvement.

Nonetheless, many companies do use “generic” segmentation schemes.  They need to satisfy themselves that in doing so:

  • The generic scheme satisfies the criteria set out below; and
  • That they do not risk being at a disadvantage to competitors who use a customized segmentation scheme.

Criteria for Selecting Segmentation Basis

The market segments identified should satisfy three criteria. 

Internal Homogeneity/External Heterogeneity

This means that potential customers within a segment should have similar responses to the marketing mix variable of interest but a different response to members of other segments. 


This is the degree to which the segmentation makes every potential customer a unique target.  That is, the segmentation should identify a small set of groupings of substantial size.


This is the degree to which marketers can reach segments separately using observable characteristics of the segments.

How Market Segmentation can Increase Profit

Increasing profits is the major objective of companies.  We can write the profit for a product down as a formula:

Profit = Volume*(Price - Variable Cost) - Marketing Costs - Fixed Costs

There are several ways in which effective segmentation can boost profits.

  1. By better meeting customer needs, through better positioning to chosen segments, we may be able to increase market share and hence volume.

  2. By better meeting needs, we may also be able to increase price without sacrificing much volume.

  3. By only targeting the most profitable segments, we may be able to reduce marketing costs.

Methods of Market Segmentation

There is a large array of analytical techniques applicable to market segmentation.  The most frequently used are briefly described below.

Cluster Analysis

Cluster analysis is a set of techniques for discovering structure, or groups of individuals, within a set of data comprising measures on each individual.  The measures could be, for example, an attitudinal battery.  There is no dependent variable – all variables are treated equally.

Conjoint Analysis

This technique aims to decompose preference into component parts, such as brand, quality, and price.  This technique views products as bundles of attributes and uses an experimental design to vary attribute levels to create product descriptions.  Survey respondents then rank the products and the analysis works out how much each attribute contributes to preference.  It is a good technique for benefit segmentation.

CHAID/Regression Trees

This was called Automatic Interaction Detection for a long time and now also goes under various names used by software vendors, including Regression Tree, Answer Tree, Classification Tree and CART.  It is a technique frequently employed in Data Mining and it is a useful exploratory analysis technique prior to regression analysis.  It can quickly analyse a large set of candidate explanatory variables to determine the most influential variables on a dependent variable.

The basic idea is to hierarchically segment the population on the database based on a dependent (categorical) variable such as bought/did not buy a product.  The explanatory variables are categorical too, such as: 

  • Demographic variables (age group, gender, occupation);
  • Attitudes (agree/disagree with various statements);
  • Previous behaviour (bought/did not buy another product).

Discriminant Analysis

This technique is used to quantify the relationship between segment membership (eg bought, did not buy) and explanatory variables such as income and attitudes.  It is often used after CHAID identifies candidate explanatory variables, to formally quantify and test the significance of relationships. 

Further Information

Market Segmentation as part of Marketing Strategy

“Marketing Engineering” by Gary L. Lilien and Arvind Rangaswamy, Addison-Wesley, is an excellent book on the application of marketing models to marketing strategy.  It includes Excel-based software to demonstrate the application of these techniques.

Multivariate Analysis

A good text on the technical details of the whole range of techniques for segmentation and more is “Multivariate Data Analysis” by Joseph F. Hair et al, Macmillan.

Cluster Analysis

For details of algorithms, see “Finding Groups in Data, An Introduction to Cluster Analysis” by Leonard Kaufman and Peter J. Rousseeuw, Wiley.

Some micro market segments in USA

The book Micro Trends: the small forces behind tomorrows big changes by Mark J Penn with E Kinney Zalesne describes what are really consumer lifestyle segments (Hachette Book Group, 2007).  Those identified are small as a proportion of the population but in a country with a large population, this is quite valid.  A very interesting book.

Market Segmentation and Forecasting

Conjoint Analysis

Marketing return on investment (ROI)


Interested in forecasting?  A new book - Forecasting: the essential skills (Part 1: case studies) is available.

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Did you find this information useful and would you like some case studies?

If so, please consider donating $10 (Australian dollar = about $9US or about 7 Euros) so that we can continue to improve this page and we will send you by email some segmentation case studies including the batteries of questions and details of the segments.  The case studies include attitudes towards advertising, towards food, and price sensitivity.  Your donation will be via PayPal.  The Microsoft Powerpoint document will be sent to you, usually within 48 hours although at peak periods it may take longer.