How you can use data to better target buyers
There are lots of ways to segment your audience. These typically fall into four categories: who, why, how and where. The who includes demographics like age and gender. The why includes data like interests and motivations. The how would be focused on past behaviours like purchasing, website visits and other trackable actions. The where deals with geography.
What is geo-demographics?
Geo-demographics is the study of a population and characteristics divided by regions. It’s applying filters amongst target audiences that leads with the assumption that those that live in the same area are more likely to be similar than two random people. The area in which they live may mean they have similar economic characteristics as well as preferences and backgrounds. Neighbourhoods can then be categorized by the people that live there. Then, similar neighbourhoods can be grouped together even if the physical distance is great. The most important source of geo-demographics is census data.
Origins of geo-demographics
Segmenting by location isn’t new. Its origins date back to the late 19th century in England. Charles Booth, a social researcher, spent years studying poverty in London. He then began to “classify”neighbourhoods. This classification actually led to many programs to help the less fortunate. He could actually be considered the grandfather of geo-demographics. From his work, commerce began to see opportunities to use geo-demographic targeting. It provided a way to better segment buyers based on their ‘where’ and that those around them had similar backgrounds.
Now, geo-targeting has the power of demographic data, consumer classification and consumer intelligence. The combination of all this data can provide brands the power to target audiences even better.
The pillars of segmenting
Within segmenting, there are certain pillars that help create profiles. Let’s look at these more in depth.
There are many areas to look at within geographic. The geographic attributes are often indicators of a person’s lifestyle. The first segment of urban versus rural is very significant in understanding a buyer. People who live in the city versus the country have very different lives. City dwellers have access to much more than those in rural areas. Therefore, there’s much more competition for a city dweller’s attention. Whereas, a rural consumer may tend to shop more online because there is less access to brick and mortar shops.
Choosing to live in the city also may mean that a person works a professional job and has a higher education. Rural residents may have more manual jobs, like factory work or farming.
Actual locations can also be geographic. Consider postcode insights. Looking at groups based purely on their postcode can provide a wealth of information. What do they have access to in the way of retail? What type of transportation might they take based on their location? The answers to these questions can help marketers target better.
Climate is another topic that comes under geographic. Those that live in cold weather areas have different needs than those that live in warm climates. They wear different clothes (thick coats vs. swimwear), they participate in different actives (skiing vs. golfing), and they need different tools or items (snow shovel vs. mosquito nets). That’s just looking at cold versus hot. There is also differentiation based on other climate attributes like dry, humid or wet. Those that live in the desert don’t need lots of lawn care accessories. Those that live in areas that experience lots of rain don’t need drought related supplies.
Market size is another segment of geographic. It’s a bit similar to rural versus city. However, it is different because city size matters. There are many areas considered metro; however, they can’t compete with the market size of London. The larger the metro area, the more assumptions that can be made for those that reside there.
Demographic data is the ‘who’ part of the puzzle. This is where you’ll find age, income, gender and ethnicity. By adding the demographic data to what you already know through geography creates an even more specific target audience. This data provides physical attribute information as well as their income bracket and therefore their spending power.
This portion deals with preferences, personality, interests and motives. It’s much easier to understand a buyer’s likes and dislikes in the 21st century. Think about the way a social media profile is set up. A profile is tied to what brands people like or their interests. This type of data can also be compiled by market research. This type of segmentation complements the other pillars of segmentation. Insights from psychographic segmentation enable marketers to understand consumers’ decision-making processes better. Then their messaging can be more relevant.
This is the ‘what’ of a target audience. It divides a group based on their behaviour. The data related to consumer behaviour can be very valuable. It’s what actions the consumer takes in their consideration and decision to make a purchase. This type of data is determined by what a consumer has purchased and drawing conclusions about future purchases. It can also be related to the time spent on a website or the pages visited. These are all macro actions taken that then turn into conversions or abandonment.
Data modeling using OAs
With all this powerful data, now you need to put it into action. Data can be modeled using OAs, as these are of comparable size. Another option is to market based on the postcode level. Either of these data sets is accessible from DoordaStats. Specific audience profiles can be constructed using these models.
Geo-targeting use cases
This is the actionable part of the story. What can you do with all this important data? Map the postcodes to customers and create a full view of their lifestyle. Create targeted profiles and use latitudes and longitudes to target customers digitally. Identify where to open new stores based on various pieces of data that would make a certain location desirable.
Geo-demographic data sources
Most geo-demographic data is derived from census information but many additional sources are available. The Land Registry, Home Office and Department for Works and Pension datasets can add a new perspective.
If you include reported crime, property sales and benefit claimants to your segments your understanding of a group or area increases dramatically.
For example, one of the best ways of identifying poverty in the elderly is to look at pension credit data. This is a top up benefit paid to those in receipt of a pension whose income is to low.
How to start use geo-demographic data
Now you know the basics why not try creating your own geo-demographic groups? DoordaStats gives you access to 99 datasets you can slice and dice any way you like.
It’s time to segment better. The data is available so it’s time to put it to work. Sign up today with DoordaStats for a free sample.