When it comes to defining the target audience of your marketing campaigns, you may have heard about the concept of market segmentation.
Segmentation is an increasingly popular practice among marketers to ensure messaging is more personalised – something that 63% of consumers have come to expect as standard, according to RedPoint Global. So, let’s take a closer look.
What is segmentation?
Segmentation is when we place customers or potential customers together into groups, based on a set of identifiable characteristics.
Splitting up a large target audience (e.g. all businesses of a certain size) into more narrowly defined target groups is useful for various reasons. Firstly, with a very large target audience, it’s difficult to create messaging that resonates with each individual in the group. Breaking the audience down allows tailoring of more relevant messaging.
Additionally, segmentation can help you to better understand the appeal of your product, based on the response of different sections of your overall market. This allows you to build up a clear picture of their interests and motivations, in order to better position your product to them or even tailor your product to match different needs.
Finally, segmentation can help to reduce unnecessary marketing effort or ‘waste’. By targeting consumers more specifically, you avoid paying to advertise your products to people who will never buy them. This is particularly pressing, since only 9% of ads receive more than a second’s worth of attention, according to Lumen.
Different types of segmentation
So, how does segmentation work to differentiate consumers based on certain characteristics?
Usually, segmentation is done on traits such as age or gender, but it is also possible to segment using behavioural traits such as buying habits, or even underlying psychological traits that can be inferred through other behaviours. There are many types of segmentation, but here we will broadly group them into four types. As we will see, each has its relative merits and drawbacks.
Demographic segmentation is generally the most simple and straightforward method of segmenting customers using their demographics. This includes traits like age and gender. Whilst this type of segmentation can be useful – and some marketers would argue is the most effective – it is often superficial and does not give us great insight into why a specific customer has signed up to an email list, liked a post or bought a product.
A past client summed this up well when they started working with us: “Our customers include a lot of 45-year-old married white men, yet some of them buy our product and some of them don’t”.
It’s important to be aware that demographic segmentation does not give us any insight into the motives behind our customer’s decisions. It is, however, an effective starting point.
Geographic segmentation is a subset of demographic segmentation. Here, we identify different target customer groups based on their location.
This differs in a use case from simpler demographic segmentation because it works on the principle that individuals tend to have needs, preferences and interests that are in line with their geographies. Geographic location can be strongly correlated with other factors relevant to positioning, such as socio-economic grouping informing the price point.
For this reason, geographic segmentation, along with other types of demographic segmentation, is open to criticism of introducing bias into how targeting is performed.
The third type of segmentation is behavioural. This is what gives us “you may also like” suggestions and relies on observing similar behaviours within different sets of users. This is a useful practice, as it helps marketers develop a more targeted approach to their marketing by looking at buying patterns in the customers they are targeting.
However, it is also susceptible to generating user experiences akin to echo chambers, as recommendations loop round in an algorithmic “if this, then that” fashion. Without a solid foundation guiding such recommendations, they often miss the mark and feel hollow, ultimately alienating people and hemming them into a limited experience of a brand.
For example, consider a customer with a loyalty card which they only ever use at a petrol station. This does not necessarily mean they only buy petrol, snacks and coffee. Indeed, tailoring offers inline with these buying behaviours alone is unlikely to be effective and it is therefore necessary to look at underlying patterns. This could allow us to identify that the customer shops early in the morning and prefers healthy snacks – ultimately giving us a clearer picture of how to present offers to each individual.
Overall behavioural segmentation helps us understand patterns in a person’s behaviour which can help us personalise the messaging we send them. But, like all forms of segmentation, it has limitations that we need to be aware of when using it. Reducing an individual to their behavioural patterns is an imperfect proxy for their wants and needs, which are, at the end of the day, individual.
Overall, segmentation is a relatively effective approach to tackling the huge issue of personalisation. It’s hard for us to understand each and every one of our customers, and practically impossible when you’ve got thousands, even millions, of them.
In the near future, AI and machine learning will help to automate this process and offer truly personalised messaging on the spot. But – for now – segmentation offers a reasonable middle ground for us to better understand and target our audiences.
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