Wells and Gubar Lifecycle Stages
Category: Marketing
Bachelor stage (young single people, not living with parents)
Newly married couples without children
Full nest 1 (youngest child under 6)
Full nest 2 (youngest child 6 or over)
Full nest 3 (older married couple with dependent children)
Empty nest 1 (no children living at home, family head in work)
Empty nest 2 (family head retired)
Solitary survivor (in work)
Solitary survivor (retired)
SOURCE: Consumer Market Research Handbook, 3rd Edition R Worcester and J Downham Editors, McGraw Hill
One major criticism of Wells and Gubar’s schedule can be made however it is not exhaustive. For example, «couples without children» are not included. However they may be included at stage 2.
If a segmented strategy is to be based upon demographic differences, then the segments need to be identified, isolated and reached. Disposable nappies have traditionally been targeted at two gender segments: mothers with babies and expectant mothers. In order to «reach» these segments with appropriate promotional messages, information on the attitudes and behaviour of mothers and expectant mothers must be obtained. This has led to the development of a special kind of market research in the form of ‘baby panels», composed of samples of actual and expectant mothers who regularly report their purchase and consumption of baby products. Analyses of their media exposure patterns can indicate whether a media schedule can be derived which would give controlled coverage.
As Oliver (1990) reports, consideration of a company’s relative appeal to various demographic segments can lead to re targeting and re positioning strategies. Oliver cites the case of Kentucky Fried Chicken, which undertook a major re-targeting promotional campaign following demographic research which showed that one third of its business came from young males after 10 o clock at night. Outlets were therefore operating at well below capacity for most of the day. The new promotional campaign involved advertising targeted at families and groups of young adults, in order to encourage these demographic segments to use Kentucky outlets during the daytime. Re-positioning was also achieved by product, packaging and service (staff uniforms) alterations.
3. Socio-economic and income:- This set of segmentation variables is based upon differences in consumption patterns being related to social class arid/or income level.
Martineau has asserted that a rich man is not just a poor man with money. To some, a class system is iniquitous because the heart of the concept if inequality. Others recognise class distinctions as a good thing, and inevitable, because society values the relative contributions of individuals differently. What is clear is that in all societies people do different jobs, receive different levels of reward, live in «better» or «worse» districts, in «better» or «worse» housing, receive different education, mix differentially with other members of society, and have different belief systems. If enough people have any given mix of these variables, then they could constitute a social class group.
Attempts to measure social class can employ three categories of approaches, as shown in the Table below.
Methods of Measurement of Social Class
| Subjective measures | Individuals are asked to estimate their own social class positions |
| Reputational measures | Participants make judgements about the social class of others in their community |
| Objective measures | Occupation
Income Education Quality of neighbourhood Value of Residence Inventory or quality of possessions |
In segmenting consumer markets on a socio-economic base, marketers never use subjective or reputational approaches. In the former, too many individuals emerge as middle class, while in the latter the method is too cumbersome for large-scale commercial application. By way of contrast, the objective measures are relatively easy to obtain from the answers to a few factual questions. Of these, the occupational and income factors are the most widely used in marketing, as expounded by Foxall. Indeed; most social class segmentations in the UK still use the standard occupation definitions of the advertising agencies professional body (the Institute of Practitioners of Advertising) which are: —
A, B: Managerial and Professional C1: Supervisory and Clerical C2: Skilled Manual D, E: Unskilled Manual and Unemployed. The problem with this system is that these grading are a confusing combination of social class and income factors. Attempts have been made to more clearly separate income and class factors in relation to purchase behaviour, and there is a growing body of evidence to suggest that income is more significant than social class as a correlate of buying behaviour.
Despite growing evidence of income levels being better predictors of consumption than social class, Oliver (1990) adds a note of caution in following this dogmatic line of reasoning. He suggests that the balance between the two is probably related to the specific product in question. Consumption of some products may be more related to class than income and vice versa.
In conducting socio economic segmentation research it is therefore advisable to get as wide a base of «social class» — related information as possible, combined with specific income — related information. This may allow more precise correlations to be drawn between buyer behaviour and either social class factors or income factors. Depending on the product however there may be equally strong correlations with both social class and income factors.
4. Geodemographics: — Continuing doubts about the discriminating ability of demographic, social class and income variables in segmentation, has spurred the development of alternatives or extensions. Geodemographic segmentation considers differences in the spatial distribution of key demographic variables In the UK this has come to be associated particularly with the geodemographic variable of residential neighborhood.
There are now several geodemographic analysis companies offering demographic breakdowns of particular regions and localities. CACI provide ACORN data (A Classification of Residential Neighborhoods), MONICA data (predicting the likely age groups of potential customers according to their first names) and PIN data (Pinpoint Identified Neighborhoods: — Analysing 130000 neighborhoods and identifying 60 classifications). A competitor, CNN provides MOSAIC data, which classifies neighborhoods into 58 lifestyle categories. A third company now providing geodemographic data is Credit and Data Marketing Services Ltd, providing SUPER PROFILES data. This classification links consumer lifestyles with postal geography, on the basis of 36 geo-lifestyle groups.
Use of these services in market segmentation rests on the expectation that there is a connection between the kind of housing a person has and their purchasing behaviour. Advocates of geodemographics point out that where we live is intimately connected with how we live, and that subsumes what we consume.
Geodemographic data are being widely employed by retailers as a basis for estimating market size and trends within the marketing areas of stores. In a similar way, it can be an ingredient in developing relative buying power indices for each small district, and can then be used in designing territories for sales representatives. It can also be used for accurate direct mailing.
A special edition of the Journal of The Market Research Society has been devoted to applications of geodemographics (Volume 31, No 1; 1989), and is worth consultation. In this special edition, applications in retail store site selection and development are discussed, as well as the used made by firms such as Tesco, Woolworth, Boots.
5. Benefit segmentation: — Haley first developed the idea of benefit segmentation. An example of this approach as applied to the less expensive camera market is illustrated in the table below.