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Acorn Explained

Tuesday 6 November 2018 Data Insight & AnalyticsData-Led MarketingDemographic Data

Patrick Tate's picture
By Patrick Tate

Acorn is a powerful consumer classification that segments the UK population. By analysing demographic data, social factors, population and consumer behaviour, it provides precise information and an understanding of different types of people.

But where does it come from, and how is it built? In this blog we look at the methodology behind Acorn, the data used to build it, and some important differences in classifications.
 

What is Acorn?

Acorn segments postcodes and neighbourhoods in the UK into 6 Categories, 18 Groups and 62 types, three of which are not private households. By analysing significant social factors and population behaviour, it provides precise information and in-depth understanding of the different types of people who live in a particular area.

With this information you can learn more about your customers' behaviour and identify prospects who most resemble your target customers, define local demand for products and services and understand what drives effective customer communication strategies.
 

Methodology

The methodology that Acorn and its counterparts use to create segmentations has essentially remained the same since CACI created the very first consumer classification in 1978.  Census data is used as a foundation on which to build the segmentation – it contains the same data for everyone, everywhere regardless of whether it’s relevant or not - add to this some proprietary lifestyle data, do some k-means clustering and et voila! – you’ve got a consumer classification.

The consultation, Beyond 2011, identified the need to change the way in which the Census is collected (currently it’s a pen and paper exercise) to take account of new technology, cost savings and changes to data protection laws.  We realised the implications of these changes and embarked on a 2-year investigation into other techniques and methodologies in order to create a better and more accurate version of Acorn.

In November 2013, we were the only commercial company able to demonstrate our new classification at the DMA’s decennial conference, Tracking A Decade of Changing Britain.  The new methodology to build Acorn is no longer reliant on the Census, instead we are able to effectively utilise data from a variety of sources.

One of the key methodologies allows new neighbourhoods, regenerated areas and other areas of wholesale change that have occurred since the Census to be properly identified using a specific algorithm to identify and correctly classify new build areas.  This is to ensure that Acorn is always as up to date as it possibly can be.

One of the key methodologies allows new neighbourhoods that have occurred since the Census to be properly identified

Data

So outside of the Census, what data goes into Acorn? Over the years we have researched a vast array of new data sources.  The Open Data initiative has provided a great source of new data that is constantly updated and available at small area level.

Alongside this, we buy in a number of 3rd party data sets, including a dataset of all retirement living developments, data from the Land Registry and rental data from the UK’s leading online property portal.

We have also embarked on the creation of our own datasets, such as student accommodation and the locations of high-rise residential buildings. The advantage of having these data sets allows us to segment hard to classify neighbourhoods.  As the lifestyle and consumer habits of students and those that live “vertically” differ considerably from standard residential neighbourhoods.

it is imperative that any classification used to target your customers isn’t reliant on data that is now over seven years old

Postcode Vs Household Level

Acorn is also available as a separate classification at address level called Household Acorn.  We believe that the way one describes a neighbourhood (i.e. postcode) is fundamentally different to how a household is defined. This differentiation ensures accuracy. Where the size and composition of households within a postcode significantly varies, using the same classification for both can become extremely imprecise, depending on how the classification is being used.

For example, imagine a typical street in Britain with 15 households. 14 of those households are occupied by couples in their 50s-60s, where their children have left home.  This neighbourhood can rightly be described as a community of empty-nesters.

The one remaining house in the street houses an elderly, single woman in her 80s.  A household classification would be able to identify this household in isolation from its neighbours. 
A simple calculation would tell you that there are enough single, elderly women living on their own across the country to constitute having their own household segment.  But, there are nowhere near enough neighbourhoods or streets where the dominant house type are single, elderly women.  So, a compromise must be made on the accuracy of either the postcode segmentation or the household classification.

 

An insightful view of your customers

There are many consumer segmentations and classifications allowing you to target different consumer types, all based on a methodology that CACI invented 40 years ago.  The latest incarnation of Acorn however, is something different and fresh.

With the rapidly changing nature of neighbourhoods and the speed of redevelopment happening within many of our cities and towns across the UK, it is imperative that any classification used to target your customers isn’t reliant on data that is now over seven years old.

Acorn utilises the latest data alongside a new methodology to give the most accurate and insightful view of your customers, service users and prospects available.

 

You can find out more about Acorn and discover which Acorn segment your neighbourhood belongs to here, view the product sheet, or get in touch to find out how Acorn can help your business

Acorn is a powerful consumer classification that segments the UK population. But where does it come from, and how is it built? In this blog we look at the methodology behind Acorn, the data used to build it, and some important differences in classifications.

Acorn Explained