Geodemographics

A detailed Summary of Geodemographics

Geodemographics is part of demographic segmentation, which is a market segmentation process. Market segmentation is a process of identifying segments of customers within a particular market whose needs and wants differ, so a different marketing mix is applied to each. Demographics include the identification of the market by age, gender and lifestyle. The geography aspect deals with regions, climate, size and density. Geodemographics is a fusion of demographics with geography, hence its name, and it deals with units of neighbourhoods.

The fundamental basis of reasoning assumes that people who have similar demographic characteristics, (like age, income, and occupation) tend to live in similar areas. The areas in which these people live also mould their lifestyles. For example, the lifestyle of a young man in London will very much differ from that of a farmer's son in Norwich. Assuming that people that live in the same neighbourhood have many things in common, I believe is a good one. Common sense tells us that neighbourhoods are made up of similar people because they live in similar houses, drive similar cars and do similar jobs (have similar demographic characteristics). For example in my neighbourhood, 90% of the houses are semi

articular group further. It is useful in identifying the location and lifestyles of potential customers, by product and by category and that is why a number of firms use it.

Finally, I believe that if firms wish to locate their customers and build strong relationships and understandings with them, then geodemographics must be used. It is a realistic way to identify groups of consumers and many large firms have benefited from its qualities.

ork, mow their lawns on the weekend and visit family on the Sunday. Similar views and opinions are shared and this is reflected in local petitions and elections, etc. Because people in a certain area have similar lifestyles, it is for this reason that geodemographics is a good segmentation technique. However, it must be taken into account that times are changing and there are a few individuals in my neighbourhood for example, that don't share the norms of the area. In 10 years time the picture might be more different too. For example, more people now are commuting longer distances to work and don't necessarily live in a neighbourhood near to their place of work. Also, newer housing estates are made up of various types of housing, thus attracting different kinds of people, so while geodemographics is a good segmentation technique for now, in a few years it will probably need to be reviewed.

Secondly, the fact geodemographics are neighbourhood classifiers, rather than individual classifiers, is a weakness in applications like direct mail targeting. It is clearly less precise to mail households in a neighbourhood, about whom you can model a probability, than to mail an individual about whom you know something for certain. Therefore, data can only be used at neighbourhood level, and targeting of individuals is not possible - Firms can know a vast amount about the neighbourhood within which an individual resides, but know nothing specific about the individual himself. Therefore, if a firm wants to target an individual for a particular brand or product, they couldn't. They could only identify the area in which they most probably lived. To target the individual geodemographics has to be linked with other techniques. For example, if you are planning to send a mailshot to an individual, geodemographics will tell you about the characteristics of their neighbourhood, howev!

For example, the Swedish furniture company, IKEA uses it to analyse its customer base. It helps them to define its local distribution plans for its catalogue and to evaluate how effective previous distributions have been. To do this they analyse customer data to see where they are coming from, and also their level of expenditure. This helps them to predict likely returns on its investments in the next catalogue distribution. They also look at the size and frequency of the purchases and the distance the customers live from each store. Using this information alongside ACORN classification types has allowed IKEA to improve their

Some common words found in the essay are:
Marketing Systems, Norwich Assuming, Residential Neighbourhoods, Mosaic Super, , Britain L54, people similar, people live, market segmentation, census data, people similar lifestyles, similar demographic characteristics, similar cars similar, similar cars, geodemographics segmentation technique, lifestyles example, live similar, live neighbourhood, cars similar, particular product brand, firms decide,

Approximate Word count = 1988
Approximate Pages = 8 (250 words per page double spaced)

Category: Miscellaneous