The Experian/Acorn model

These are models well known to the marketing experts of large companies. They represent the techniques used by two of the largest players in the so called "geodemographic and lifestyles industries". In effect, these models are people spaces that seek to map the relevant details of large populations of people in an n-dimensional space for marketing purposes.

Experian (now a subsidiary of Great Universal Stores) with annual sales around £1 billion / $1.5 billion and employing more than 12,000 people, maintain records on millions of consumers. Through a variety of sources, they create socio-economic profiles of consumers, categorising them according to age, income, family status, credit rating and all manner of other classifications that turn up on the billions of records that are sourced for information.

This huge volume of data can then be subjected to exploratory analysis, or 'data-mining', using sophisticated programming tools - such as, automated cluster analysis, probability modelling, neural networks, genetic algorithms, etceteras.

Founded on simulation technology in 1962, CACI International Inc (www.caci.com) have developed a system called ACORN™ (A Classification of Residential Neighborhoods) to underpin many of their various services. It is a demographic information system, based upon a people space that categorises people according to where they live. The general idea is that residential localities can give indications of life style and spending patterns. This allows businesses to more efficiently target particular sections of the community. From this people space, CACI has evolved a diverse solutions portfolio for the Net economy. With approximately 5,000 employees and more than 90 offices in the US and Europe, CACI integrates the networks, systems, and software for telecommunications, e-Commerce and marketing information.

Clearly, such customer profiling and lifestyle databases are immensely costly to build and maintain. They require large revenue streams, so, the benefits can be made available only to very large organisations who can afford to pay the necessary fees. Customers benefit from this system only indirectly, through the questionable advantage of having their needs targeted more specifically. The disadvantage is that these needs are approximated to cater for the statistically significant averages and are biased heavily towards the products of the suppliers who can afford to use these customer targeting services.

Turning this system on its head would involve handing the construction, control and running of such a system over to the customers: letting them enter their own data and choosing for themselves the suppliers who would be most likely to satisfy their needs. This is what we are proposing to do.