What is geodemographic profiling?
More than 64 million people live in the UK, each with their own outlook, priorities, needs, and way of life. In this rich tapestry, trying to identify the right audience for your service or product can seem impossible. The key to success is being able to identify distinct groups of people whose lifestyle and needs fit the product or service you are offering. If you are fortunate, you may have an existing database of like-minded individuals that you can approach, or the budget to use mass broadcast campaigns to get an audience to come to you. For the rest of us, an effective solution is geodemographic classification and targeting.
Categorising the UK population
Geodemographic classification is an in-depth study of people and locations. It attempts to provide accurate and detailed descriptions of UK consumers and the areas where they live. Geodemographic classifications are based on three simple principles:
- People who live in the same location are much more likely to have similarities than two people picked at random from different areas
- Locations are classified according to the characteristics of the people that live there
- Locations will be assigned the same classification if they contain similar types of people, even if they are situated miles apart
Geodemographic classifications use data from public or private sector sources to categorise people. Information about UK consumers is then presented in the form of demographic types. Think of geo as the places and demographic as the people.
- Demographic data: Age, ethnicity and country of birth
- Household data: Family type, size and living arrangements
- Housing characteristics: Tenure, type and size of home, plus quality of housing such as overcrowding or lack of central heating
- Socio-economic factors: Education, car ownership, commuting and health
- Employment: Status, level of activity and type of employment
- Economic activity: Employed, self-employed or unemployed, industry, grade, full or part time
These classifications allow organisations to make informed decisions about where to site their stores, who to target with marketing campaigns, and which areas will benefit most from the services they offer.
Finding the right data recipe
The trick to producing – and using – a demographic profile is to strike the right balance between trying to account for everyone as a unique individual and resigning yourself to everyone being the same. As such, each profiling tool has its own unique recipe of data sources and will provide slightly different results.
Most demographic profiling data is based around a broad base of national data such as the census with additional data appended to add depth. The census is a good base of data as it is:
- Compulsory: every UK resident is legally obliged to answer the questions with the exception of those in Northern Ireland
- Comprehensive: the census covers a very wide range of questions
- Consistent: every person is asked the same questions and it takes place every 10 years
Some providers have their own credit or purchase history data which they add into the mix, whilst others may use further national or local survey data to provide depth to the data. This varying data recipe means that each distinct set of profiling data is different and tends to be better suited to certain uses.
To give an example, our own P² People & Places demographic profiling data is based around census data but enriched with Living Costs and Food Survey (LCF) and British Population Survey (BPS) data. Its independence from localised commercial or credit data means it provides maximum discrimination between the different profiles, especially the less affluent across the UK. This makes it ideal for a local authority trying to identify the poorest and most vulnerable members in the community, who will not have a detailed credit history.
As such it is always worth looking at several providers’ recipes before deciding on the best geodemographic profiling data for your organisation to use. Each layer of data adds to the cost, both through the data itself and the time needed to map it to the base dataset. Both the number of layers and the type of data itself can cause the costs of different demographic datasets to vary wildly. You will need to balance your needs against your available budget.
Adding a new dimension to your business intelligence
Applied to a customer/member database, it can add a depth to your customer understanding, even if all you hold is their postcode. When mapped to purchase habits and history, it allows you to identify the demographic groups that are likely to spend more with you and use this knowledge to identify new potential audiences around the country.
This customer data analysis can be applied when selecting a new site for retail outlets, or assessing the merit of existing sites. If you lack a customer database, you can apply profiles from similar businesses, or even use the abstract descriptions that accompany each profile to identify a potential audience.
Amongst not-for-profit organisations, geodemographic profiling can help to identify localised needs for particular support or services, or identify people with a higher propensity to donate, if they are looking to raise funds.
In short, geodemographic profiling has many uses and provides an effective way to improve business intelligence and service application, with a direct improvement in efficiency and profit.
An example profile: postcode area YO8 3RG
A rural location classified at the broadest level (our own P2 profiling uses the term “Tree”) as
C: Middle England, which is summarised as:
Rural, established families in detached and semi-detached houses. Well-qualified managers and commuters with some agricultural workers, enjoying a social life and varied holidays.
At the next level of detail (we call these “Branches”) the classification is:
C08: Owner Couples, described as:
Rural families in large houses, working locally in agriculture and the service sector.
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- Social change over 10 years?
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- What is the Living Costs and Food Survey?
This often overlooked data set provides some very useful data to help us understand consumers.
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