With the use of psychographical data, researchers had been able to segregate consumers into a number of life-cycle and lifestyle groups.
Moschis used both gerontological and psychographical variables (together known as gerontographical variables) on older people to produce a life-stage segmentation model comprising healthy indulgers, healthy hermits, ailing outgoers, and frail recluses (Moschis 1996).
The traditional method of segmenting consumers that we have explained emphasises the use of personal characteristics comprising demographical, socio-economic and psychographical data as independent variables.
Psychographical data are sometimes used interchangeably with lifestyle data (Gonzalez & Bello 2002) and product attributes/benefits are sometimes categorised as psychographical data (for example, see Ailawadi et al.
Psychographical variables, however, do have a number of limitations.
Although psychographical data, such as those relating to values and norms, generally remain stable over a long period of time, other data, such as those relating to individual interests and opinions on specific issues (for example, politics), tend to change over time.
At a general or global level, psychographical characteristics comprise a wide range of data, meaning that it is impractical to capture all of them.
These benefits, when they are present as attributes of a product, service or market offering, cause consumers to purchase those products, rather than merely describe who they are as consumers in terms of socio-economic, demographical or psychographical data.
An exploration of potential relationships between psychographical characteristics and the benefits sought could generate homogeneous segments of older consumers that can be identified and targeted more easily than if they were identified only in terms of psychographical characteristics or benefits sought.
For many companies, the interactive psychographical modeling process presented can be used to gain a better understanding of a firm's current customers and to develop products, services, and communications to enhance the lifetime value of those customers.
Although the categorization of consumers on psychographical dimensions was first introduced over 30 years ago, it is still one of the least understood and researched concepts in marketing (Heath, 1995).
To be able to classify all of the bank's customers for which we did not collect psychographical data, a set of customer-scoring models was developed using logistical regression.