ACORN and Related Classificalorv Systems

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As a direct challenge to the socioeconomic classification system, the ACORN (A Classification Of Residential Neighbourhoods) system was developed by the CACI Market Analysis Group.1 The system is based on population census data and classifies residential neighbourhoods into 54 types within 17 groups and 6 main categories. The groupings were derived through a clustering of responses to census data required by law on a regular basis. The groupings reflect neighbourhoods with similar characteristics. (Table 1 shows how the main categories break down into increasingly small subgroups and types.)

Early uses of ACORN were by local authorities to isolate areas of inner-city deprivation (the idea came from a sociologist working for local authorities), but it was soon seen to have direct marketing relevance, particularly because the database enabled postcodes to be ascribed to each ACORN type. Hence its use particularly in direct mail marketing.

The introduction ofCACl's ACORN geodemo-i;rapMc database represented one of the biggest steps forward in segmentation and targeting techniques. Although the measure is crude, the great strength of the sendee depends on CACI's own research linking the neighbourhood groups to demographies and buyer behaviour, together with the ability to target households. The system, therefore, provides a direct link between off-the-peg segmentation and individuals, unlike earlier methods which only provided indirect means of contacting the demographic or personality segments identified.

Like die other a priori techniques, the limitations of CACI's approach are the variability

within neighbourhoods and the similarity between their buying behaviour for many product classes. English2 provides an example of this where five enumeration districts (individual neighbourhood groups of 150 households) are ranked according to geodcmo-graphic techniques. Of the five, two were identified as being prime mailing prospects. However, when individual characteristics were investigated, the five groups were found to contain 31, 14, 10, 10 and 7 prospects respectively: the enumeration districts had been ranked according to the correct number of prospects, but neighbourhood classifications alone appeared to be a poor method of targeting. With only 31 prime target customers being in the most favoured enumeration district, 119 out of 150 households would have been wrongly targeted.

IKEA's Catalogue Targeting1 Many companies now use geodemographic segmentation to help their decision making. IKEA, the Swedish furniture retailer, used it to analyze its customer base. The store provides a vast range of stylish and original furniture, fittings and fabrics at affordable prices. The IKEA concept is a simple and effective one that has worked throughout the world. The company retails from large out-of-town stores and sells furniture in easy-to-assernble kit format, passing on the cost savings it gains from this to the customer.

A key element in IKEA's success is its catalogue: produced once a year, it features a broad selection of products, showing the depth and breadth of the range available in the stores. The company's local catalogue distribution around each of its stores represents a large promotional investment. Geodemographic analysis of its store catchment areas helps IKEA define its local









19. S





















Wealthy achievers, suburban areas



Affluent greys, rural communities



Prosperous pensioners, retirement areas







Wealthy suburbs, large detached houses



Villages with wealthy commuters



Mature affluent home-owning areas



Affluent suburbs, older families



Mature, well-off suburbs


Source:1 CACI Market Analysis Group.

buyers through to the top of the range for those who want die very best that money can buy. The company used geodemographics to understand more about customers' perception of the luxury Sottini range. The company knew that a very distinctive type of customer bought the Sottini range: it wanted more detailed information about these customer types so that it could target dealerships more accurately and provide local dealer support lor the Sottini product.

Sottini is sold through a network of independent retail outlets. As such, the company had only limited Information on its end customers, collected from responses to advertisements. CACI was able to take this information and substantiate it with data from the Target Group Index survey of people with high levels of spend on bathroom suites and home enhancement. CACI profiled these data using the ACORN consumer classification. This analysis generated a strong profile that showed that the Sottini range was bought primarily by wealthy achievers in suburbs and better-off retirement areas. A typical Sottini

Source:1 CACI Market Analysis Group.

distribution plans for the catalogue and to evaluate how effective the previous distribution had been. To do this, IKEA analyzed its customer data to sec where customers were coming from, and also their level of expenditure before and after the distribution. This helped IKEA predict likely return on its investment in the next catalogue distribution. The analysis also looked at the size of purchase, the frequency of purchase and the distance its customers live from each store. Using this information combined with its ACORN classification types has allowed IKEA to improve understanding of the relationship between eaeh of these elements. In addition to determining the postcode sectors that offer best potential for catalogue distribution, this information will help IKEA across its marketing mix to assess other promotional opportunities.

Targeting Sottini's Customers4 ideal Standard is a leading manufacturer of bathrooms. Its range covers a wide selection of prices and styles, from cost-effective suites for first-time customer would have a large disposable income -with their mortgage paid oft' and cash to spend -and would live in an affluent area.

The information helped find large concentrations of Sottini's target market and define the optimum catchment area for the Sottini dealers. Comparing the customer profiles with the catchment areas of dealers showed where to concentrate marketing support.

As a second phase to this project, Ideal Standard is looking at individual dealers. Within each dealer's catchment, specific postal areas of highest customer potential can he identified. The dealers can then use this information for direct marketing campaigns or door-to-door leaflet dis-trihutions to raise awareness of the Sottini product and inform people about their local Sottini dealership.

Kelvin Baldwin, Commercial Executive at Ideal Standard, states: the 'analysis helped us to evaluate potential for the Sottini brand across the country. We are now assessing how we can use this information at individual dealership level to aid us in dealer-support activities for our Sottini products, such as identifying new customers and locating new areas for the introduction of the Sottini product.'

To he fair, like other means ot' off-the-peg segmentation discussed, geodemographics are powerful when related to products linked directly to characteristics of the neighbourhood districts: for instance, the demand for double glazing or gardening equipment. Even in the case described above, targeting the best enumeration districts increases the probability of hitting a target customer from less than 10 per cent to over 20 per cent, but misses are stil! more common than hits.


1. ACORN User Guide (London: CACI Information Services, 1993).

2. ,1. English, 'Selecting and analysing your customer/market through efficient profile modelling and prospecting*, Institute of Internationa] Research Conference on Customer Segmentation and Lifestyle Marketing, London, 11-12 December 1989.

3. Mark Mulcahey, 'GACl's customer analysis helping IK!-;A define their target markets'. Marketing Systems, 9, 1 (1994), p. 11.

4. Julie Randall, 'CACI working with Ideal Standard to identify their optimum dealership areas'. Marketing Systems, 9, 1 (1994), p. 12.

Geodemographics is developing fast. Databases are now available in all the large economies. ACORN has been joined by FIN (Pinpoint Identified Neighbourhoods), Mosaic and Super Profile. In the Netherlands, the Post Office and Dutch Reader's Digest have produced Omnidata based on telephone subscribers, and, in Sweden, Postaid is run by a subsidiary of the Post Office. Both these systems arc voluntary, and are sold to consumers as away of avoiding junk mail. The power of basic geodemographic databases is being increased by linking them to consumer panel databases. This allows trends to be tracked: for example, over a four-year period, 28 per cent more people living in 'less well-off public housing' took package holidays.17 CCN Marketing has since extended this process to cover the EU using its EuroMOSAIC (Table 9.2).

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