Types of customer segmentation models
There are many ways to divide a group of people into smaller segments, and some of them will work for your brand better than other models.
The following are the most common types of customer segmentation models:
Demographic
Demographic information is population-related data, such as age, level of education, gender and sexual identity, racial or ethnic background, income, relationship or parental statuses, and similar characteristics. Demographic models create groups based on broad similarities between people in those categories, such as men or women of a certain age, or “luxury” brands aimed at folks in high income brackets.
Behavioral
Behavioral information includes past observed behaviors that may indicate a customer’s decision-making process or possible future interactions with your brand. This data may include their purchase history, social media platforms of choice, and other habits. For example, behavioral segmentation may include customers who have purchased your products or interacted with your brand’s social media.
Psychographic
A psychographic model considers a customer’s beliefs, attitudes, habits, personalities, or interests. If a customer survey asked, “Do you agree with the following statement?” and then presented a belief or idea, the customer’s response to that question may put them in one segment or another. This may be a useful model for lifestyle brands trying to attract customers who align with the brand’s philosophy.
Geographic
The geographic model divides people based on where they are located. This may include data like regions of a country, like the East Coast of the United States, or smaller regions, like towns or zip codes. It may also consider whether your customer is located in a suburban, rural, or urban area. Climate differences could also be a variable, such as areas that get snow or have dry seasons.
Technographic
Technographic segmentation depends on the devices and technological services your customers use. Data for this model may include the customer’s variety of mobile devices, social media services, and software or hardware preferences. For example, video game developers may create a technographic segment that includes only people using the consoles where their games are available.
Firmographic
Many business-to-business (B2B) services will use firmographic segmentation to divide the companies they want to work with into smaller groups. Firmographic data includes information like the company’s size, industry, location, growth trends, and ownership structure. For example, a software company targeting small businesses may target smaller size companies than a software company targeting enterprise public corporations, and firmographic data can help with that differentiation.
Needs-based
Needs-based segmentation models target customers who require a specific type of product or service. Returning to the pet food brand example, their needs-based model would send dog-related product offers to dog owners, but not customers who don’t own dogs.
Value-based
Value-based models focus on successful sales and existing customers. These models will look at the customers who have had the most repeat purchases or the highest returns on investment (ROI). Placing this focus on existing customers can help your organization target marketing to people who already support your brand, which helps build trust and loyalty. A Harvard Business Review study found that it costs five to seven times more to acquire a new customer than to retain an old one.
Highly targeted customer segmentation models
Depending on the product or service, industry, and available data, the customer segmentation models previously listed can sometimes be too general for highly personalized marketing. That’s where more specific segmentation models can be helpful. Some examples of these models may include:
Customer lifecycle: The customer lifecycle is a way to map how a customer journeys from considering a product to becoming a customer. A customer segmentation model based on this map would include segments for customers at each stage of that process. One segment may include customers who have never heard of your brand (awareness stage), and another may include repeat customers (loyalty stage).
Recency-frequency-monetary value (RFM) models: For an RFM model, consider the recency (how recent was the customer’s last purchase?), the frequency (how often do they make purchases?), and the monetary value (how much do they spend on each of those purchases?), and then use those groupings to identify high-value customers (HVCs).
Cohort-based: “Cohort” is a general term for any group being observed over time. This means you can define a metric within your own data, and create customer segments based on the cohort of customers who fit within it. This could include data points relevant to other segmentation models, or data you have that doesn’t fit within another model. For example, a cohort could be defined as customers who made their first purchase in December of 2023. Conducting a cohort analysis allows you to identify trends in that cohort over time (such as “what happened to these customers?”), which can help refine your customer segmentation model.
K-means clustering: This type of clustering is a statistical algorithm that identifies data points with similar characteristics, and places them in clusters together based on how close those characteristics are to each other. This is an algorithmic model that may create groups outside of the standard segmentation models mentioned above.