How to Identify Relevant Variables for Market Segmentation
This page lists the key frameworks and processes for identifying relevant variables to use when segmenting a market. The best way to identify relevant segmentation variables is to work your way through the various frameworks, identifying all the relevant variables.
Conduct brainstorming sessions with the key stakeholders.
A useful way of organizing such a brainstorming session is to give them the task of creating a segmentation tree, successively splitting the market based on key variables (e.g., splitting first based on users and non-users, then splitting each of these by another variable, and so on). Useful participants at the brainstorming are marketers, market researchers, ad agencies, people with relevant operational experience and any parties with expertise in the product category and the business problem that has motivated the need for the study. It is often advisable to avoid senior executive staff who might attempt to demonstrate leadership in the brainstorming process (as the purpose is to identify relevant variables, rather than lead). A brainstorming session should always precede any research with consumers, so that the research can be directed to validating and extending the initial list of attributes. Where consumer research precedes the brainstorming session, the outcome is invariably a list of attributes that is far from exhaustive and contains no insights that could not have been reached through brainstorming.
Identify factors relating to purchase/non-purchase
For example, conducting exploratory research where you ask people which products they do and do not like or buy, what the reasons for this are, and what the makers of the products would have to do to make them appealing enough to buy.
Observe purchase patterns
How do consumers purchase products? Marketing gossip has it that 60% of grocery purchase decisions are made in the store and take only a few seconds. If this is true, it is extremely unlikely that the factors influencing purchase are utilitarian benefits – they’re much more likely to relate to point-of-sale factors and price.
Observe how consumers use products
In the early 1970s Pepsi gave 350 families the opportunity to order home-delivered Pepsi and competitive soft drinks at discount prices. No matter how many bottles were ordered, the consumers always drank them, leading Pepsi to conclude that the volume of soft drink consumption was driven in part by whether or not consumers could get the product home. Pepsi tapped into this attribute by developing the plastic bottle, completely reshaping the soft drink market by enabling consumers to buy more product.
Identify consumers’ perception of risk
Midas has built a successful brake repair business by taking its customers through a checklist of all of that can be wrong and is wrong, thereby reducing consumers’ fears of being exploited.
Use Kelly’s (1955) Personal Construct Theory
Consumers are presented with sets of three existing products and asked to identify the attributes on which the products are similar and different. Content analysis (commonly, researcher judgment) is used to identify the underlying attributes. This approach is generally useful only when researching product categories about which little is known, or when attempting to elicit image-based attributes.
Laddering methodologies, such as means-end chains, can be a useful means of identifying relevant attributes.
Consult the academic, trade, technical and consumer literature
Consulting the academic, trade, technical and consumer literature. A good literature review can save substantial time and money. The coffee, automobile and toothpaste markets, for example, have received much attention in the theoretical and methodological marketing literature. For consumer products, magazines such as Choice and Consumer Reports, and online product comparison services like priceline.com often contain comparisons of products on what are believed to be the key attributes. Similarly, in industrial markets, technical reports and magazines often compare products based upon key performance criteria.
Author: Tim Bock
Tim Bock is the founder of Displayr. Tim is a data scientist, who has consulted, published academic papers, and won awards, for problems/techniques as diverse as neural networks, mixture models, data fusion, market segmentation, IPO pricing, small sample research, and data visualization. He has conducted data science projects for numerous companies, including Pfizer, Coca Cola, ACNielsen, KFC, Weight Watchers, Unilever, and Nestle. He is also the founder of Q www.qresearchsoftware.com, a data science product designed for survey research, which is used by all the world’s seven largest market research consultancies. He studied econometrics, maths, and marketing, and has a University Medal and PhD from the University of New South Wales (Australia’s leading research university), where he was an adjunct member of staff for 15 years.