Segmentation models: Geographic and Demographic
Geographic segmentation divides markets according to geographic criteria. In practice, markets can be segmented as broadly as continents and as narrowly as neighborhoods or postal codes. Typical geographic variables include
- Country e.g. USA, UK, China, Japan, South Korea, Malaysia, Singapore, Australia, New Zealand
- Region e.g. North, North-west, Mid-west, South, Central
- Population density: e.g. central business district (CBD), urban, suburban, rural, regional
- City or town size: e.g. under 1,000; 1,000–5,000; 5,000–10,000 … 1,000,000–3,000,000 and over 3,000,000
- Climatic zone: e.g. Mediterranean, Temperate, Sub-Tropical, Tropical, Polar
The geo-cluster approach (also called geodemographic segmentation) combines demographic data with geographic data to create richer, more detailed profiles. Geo-cluster approaches are a consumer classification system designed market segmentation and consumer profiling purposes. They classify residential regions or postcodes on the basis of census and lifestyle characteristics obtained from a wide range of sources. This allows the segmentation of a population into smaller groups defined by individual characteristics such as demographic, socio-economic or other shared socio-demographic characteristics.
Geographic segmentation may be considered the first step in international marketing, where marketers must decide whether to adapt their existing products and marketing programs for the unique needs of distinct geographic markets. Tourism Marketing Boards often segment international visitors based on their country of origin.
A number of proprietary geo-demographic packages are available for commercial use. Geographic segmentation is widely used in direct marketing campaigns to identify areas which are potential candidates for personal selling, letter-box distribution or direct mail. Geo-cluster segmentation is widely used by Governments and public sector departments such as urban planning, health authorities, police, criminal justice departments, telecommunications and public utility organisations such as water boards.
Segmentation according to demography is based on consumer- demographic variables such as age, income, family size, socio-economic status, etc. Demographic segmentation assumes that consumers with similar demographic profiles will exhibit similar purchasing patterns, motivations, interests and lifestyles and that these characteristics will translate into similar product/brand preferences. In practice, demographic segmentation can potentially employ any variable that is used by the nation’s census collectors. Typical demographic variables and their descriptors are as follows :
- Age: e.g. Under 5, 5–8 years, 9–12 years, 13–17 years, 18–24, 25–29, 30–39, 40–49, 50–59, 60+
- Gender: Male, Female
- Occupation: Professional, self-employed, semi-professional, clerical/ admin, sales, trades, mining, primary producer, student, home duties, unemployed, retired
- Social class (or socio-economic status): A, B, C, D, E, or I, II, III, IV or V (normally divided into quintiles)
- Marital Status: Single, married, divorced, widowed
- Family Life-stage: Young single; Young married with no children; Young family with children under 5 years; Older married with children; Older married with no children living at home, Older living alone
- Family size/ number of dependants: 0, 1–2, 3–4, 5+
- Income: Under $10,000; 10,000–20,000; 20,001–30,000; 30,001–40,000, 40,001–50,000 etc.
- Educational attainment: Primary school; Some secondary, Completed secondary, Some university, Degree; Post graduate or higher degree
- Home ownership: Renting, Own home with mortgage, Home owned outright
- Ethnicity: Asian, African, Aboriginal, Polynesian, Melanesian, Latin-American, African-American, American Indian etc.
- Religion: Catholic, Protestant, Muslim, Jewish, Buddhist, Hindu, Other
In practice, most demographic segmentation utilizes a combination of demographic variables. The use of multiple segmentation variables normally requires analysis of databases using sophisticated statistical techniques such as cluster analysis or principal components analysis. It should be noted that these types of analysis require very large sample sizes. However, data-collection is expensive for individual firms. For this reason, many companies purchase data from commercial market research firms, many of whom develop proprietary software to interrogate the data.
The labels applied to some of the more popular demographic segments began to enter the popular lexicon in the 1980s. These include the following:
SITKOM: Single Income, Two Kids, Oppressive Mortgage. Tend to have very little discretionary income, struggle to make ends meet.
Tween: Young person who is approaching puberty, aged approximately 9–12 years; too old to be considered a child, but too young to be a teenager.
WASP: (American) White, Anglo-Saxon Protestant. Tend to be high-status and influential white Americans of English Protestant ancestry.
YUPPY: (aka yuppie) Young, Urban/ Upwardly-mobile, Prosperous, Professional. Tend to be well-educated, career-minded, ambitious, affluent and free spenders.
DINK: Double (or dual) Income, No Kids, describes one member of a couple with above average household income and no dependent children, tend to exhibit discretionary expenditure on luxury goods and entertainment and dining out
GLAM: Greying, Leisured and Moneyed. Retired older persons, asset rich and high income. Tend to exhibit higher spending on recreation, travel and entertainment
GUPPY: (aka GUPPIE) Gay, Upwardly Mobile, Prosperous, Professional; blend of gay and YUPPY (can also refer to the London-based equivalent of YUPPY)
MUPPY: (aka MUPPIE) Mid-aged, Upwardly Mobile, Prosperous, Professional
Preppy: (American) Well educated, well-off, upper class young persons; a graduate of an expensive school. Often distinguished by a style of dress.