Geodemographic segmentation groups consumers by where they live and by the type of people who live there. It joins two of the standard bases, geographic segmentation and demographic segmentation, into one system that is usually tied to a ZIP code or postcode.
So rather than saying “our target market is families aged 30 to 45 with children”, we can say “our target market is the kind of neighborhood where those families tend to live”. And that turns out to be very useful when we have to decide where to open a store, where to drop flyers, or which customers to send an offer to.
What is the basic idea?
Geodemographics rests on two simple principles:
- People who live in the same neighborhood are more likely to be similar to each other than two people picked at random.
- Neighborhoods can be grouped by the type of people living in them, even when those neighborhoods are far apart.
The first point is fairly obvious. Housing costs filter who can live where, and people often choose to live near others in a similar life stage. A street of new four-bedroom houses near a primary school is not going to look like a street of inner-city apartments above cafés.
The second point is where the real value is. A leafy family suburb on one side of the country may have more in common with a leafy family suburb on the other side than with the apartment block two miles away. If our brand does well in one of them, there is a good chance it will do well in the others.
This is also why geodemographics is a hybrid segmentation base. We are not using location alone, and we are not using demographics alone. We are using the two together, often with lifestyle and spending data layered on top.
How are these systems built?
The companies that build these systems start with a large amount of data about small areas. Census data is the traditional base, covering things like age, household type, income, education, occupation and housing. Most systems now add other sources too, such as consumer surveys, credit and purchase data, and property data.
They then use statistical clustering to sort every small area (or every household) into groups that are as alike as possible inside the group and as different as possible from other groups.
Finally, each cluster gets a memorable name and a “pen portrait”, a short description of the typical household, how they live, what they buy and what media they use. Those names are a big part of why marketers like these systems. It is much easier to brief an agency on “Upper Crust” than on “cluster 1”.
PRIZM (United States)
PRIZM is the best-known US system. It is owned by Claritas, and the name comes from Potential Rating Index for Zip Markets. The current version, PRIZM Premier, places US households into 68 segments.
The segments are numbered by socioeconomic rank, which takes in income, education, occupation and home value. They are then arranged into 11 Lifestage Groups (based on age, socioeconomic rank and whether there are children at home) and 14 Social Groups (based on how urban the area is and socioeconomic rank).
Claritas also runs a free MyBestSegments website with a ZIP code look-up, which shows the main segments living in any US ZIP code. It is worth trying with your own ZIP code, or one you know well. Do the segments match what you see when you walk down the street?
Mosaic (United Kingdom and other countries)
Mosaic is Experian’s system. The original UK version was built at postcode level and was developed with Professor Richard Webber. Experian has also produced Mosaic versions for a number of other countries.
The current UK version puts the population into 18 groups and 68 types, each with a name such as “Lavish Lifestyles”. One point Experian makes about the newer version is that it starts with individuals, builds up to households, and only then maps to postcodes. That matters, because it means two households on the same street can be classified differently.
Acorn (United Kingdom)
Acorn comes from CACI in London. The name stands for A Classification Of Residential Neighbourhoods. Acorn works at postcode level and, in its current form, sorts UK postcodes into 7 categories, 22 groups and 65 types.
So Acorn and Mosaic are competing products in the same market. Both give a layered structure, from a handful of broad categories down to dozens of detailed types, and both come with pen portraits. These systems are rebuilt every so often, so older textbooks will quote different numbers of groups and types.
Profiling one suburb
So how would a marketer actually use one of these systems? Let’s work through an example.
Imagine a small chain of pet supply stores called Paws & Co. It has five stores and is looking for a site for a sixth. The owner has shortlisted a suburb we’ll call Maple Grove.
The first step is to look up Maple Grove’s postcodes in the geodemographic system. Let’s say most of them fall into two types. One is a type made up of young families with mortgages in newer housing estates. The other is a type made up of older couples whose children have left home, living in established houses with big gardens.
Next, we read the pen portraits. The young family type might be described as busy, value-conscious, heavy users of online shopping, and likely to have a dog as part of the family. The older couple type might be described as having more spare time and money, and spending more on their pets.
But a pen portrait is only a general description. What we really want to know is how those types behave with Paws & Co. So the third step is to run the chain’s own customer list through the same system. Each customer’s postcode is matched to its type, and we compare the mix of types among our customers with the mix in the general population.
Suppose this shows that the older couple type is much more common among Paws & Co’s loyal customers than in the population. That is a strong sign. Maple Grove has plenty of those households, so the new store looks promising.
What about the young families? They live there too, but our customer data says they are less likely to shop with us. Why might that be? Perhaps they buy pet food in bulk online, or at the supermarket. That gives the owner a choice. Do we accept that this group is not really our market, or do we design an offer, such as a delivery service, to win them over?
Geodemographics then helps with the launch. The flyers for the opening can go only to the streets where the older couple type is common, rather than to every letterbox in the suburb. That is a simple example of segmentation leading directly to a targeting decision.
What are the limitations?
Geodemographic segmentation is practical, but it is not perfect.
It works on averages. A postcode-level system describes the typical household in an area, not every household. In Maple Grove there will be young renters, retirees and single people living alongside the families. If we treat everyone on the street as the same, we will get some of them wrong. This is the reason Experian now builds Mosaic up from individuals and households.
Neighborhoods change. A suburb can shift quite quickly as house prices move or new estates are built.
It doesn’t explain why. Knowing that a household is in a certain type tells us a lot about who they are, but not much about why they buy. Two neighbors with the same profile might want very different things from a pet store. For that, we still need benefit or behavioral segmentation.
It costs money. Full access to PRIZM, Mosaic or Acorn is a paid service. A small business like Paws & Co might use free look-up tools or census data instead, and accept a rougher picture.
When should we use it?
Geodemographics is at its best when the marketing decision has a location attached. Picking store sites, planning letterbox drops and setting local media budgets are all good fits.
It is also useful when we have a customer list with addresses but not much else. Matching those addresses to a system adds a profile to every customer, which can then feed into our choice of segmentation bases.
So here is a question to try. Think of a local business near where you live. Which type of neighborhood do you think its best customers come from, and would a geodemographic system help it find more of them?
Sources
- Claritas, “PRIZM Premier”.
- Claritas, “MyBestSegments” (ZIP code look-up).
- Experian UK, “Mosaic”.
- CACI, “Acorn”.
- Wikipedia, “Geodemographic segmentation”.
