Statistical Methods for Segmentation Every Marketer Needs Now
Segment your audience using data analysis techniques like K-means clustering and RFM to understand your customers and improve marketing campaigns.
If you have ever tried to reach everyone with the same marketing message, you likely realised it did not work very well. Each person has their own unique needs, interests, and behaviours. Getting to know your audience and dividing them according to their characteristics or other traits helps you tailor your marketing.
Instead of using personas and business goals to divide an audience into groups, statistical methods are a more accurate and data-driven way to find patterns in that audience.
What is the Purpose of Customer Segmentation?
Segmentation involves breaking down your audience into smaller, more relevant groups that have common characteristics. This helps you understand your audience better, create more personalised campaigns, and set up campaigns more easily.
For instance, rather than targeting “all women,” segmentation might find a group of women in their 30s who enjoy outdoor activities, helping you create messages that align with their passions.
Importance of Segmentation
Here are three reasons why segmentation should be a fundamental part of your marketing strategy:
- Reach the right people: Tailor your messaging and offers to align with each group’s specific needs. Your message will resonate with the intended audience.
- Get to know your audience: Learn what drives your customers. This helps you build better campaigns and stronger relationships.
- Make marketing easier: Planning campaigns is easier and more focused when you use targeted groups.
Two Ways to Segment Your Audience
There are two main approaches to segmentation:
- Personas-Based Segmentation:
These are hypothetical profiles you create based on observed traits like age, occupation, or lifestyle. They’re useful for starting out but may lack the depth and accuracy of data-driven segmentation. For example, “Eco-Conscious Emily, who values sustainable products. - Data-Informedegmentation:
Using statistical methods and data analysis helps you segment your audience more accurately by uncovering patterns and clusters in their behaviour. For instance, you can identify “last-minute shoppers” by examining the timing of their purchases.
Types of Segmentation
Marketers often use one or a combination of these segmentation categories:
Many analytics platforms, like Google Analytics and CRM tools, provide access to this kind of data. However, performing your own analysis using statistics can reveal even more detailed information.
Statistical Methods for Segmentation
Here are two powerful methods to try:
1. K-Means Clustering
Clustering is a predictive segmentation method that uses mathematical algorithms to group audiences based on shared characteristics. The clusters are your segments or groups, and “K” represents the number of groups you want to create. It adjusts clusters repeatedly until it groups similar data points together.
K-means clustering is particularly useful for understanding customer behaviour and predicting future trends. You can create customised campaigns for each target segment by finding distinct clusters within your audience.
For an online retailer, clusters might include:
- Budget-conscious shoppers.
- Trend-followers.
- Frequent, high-value buyers.
How It Works:
- Step 1: Gather a dataset with variables like demographics, locations, or spending habits.
- Step 2: Import the data into visualisation tools like Tableau or Power BI.
- Step 3: Set the desired number of clusters.
- Step 4: Add more variables (e.g., frequency of purchases) to refine the clusters.
- Step 5: Repeat to identify distinct audience groups.
- Step 6: Describe your segments and create actionable personas.
💡 If you’re new to clustering, check out my K-Means Clustering for Marketers guide, where I walk you through the process step-by-step on Tableau.
2. RFM Analysis (Recency, Frequency, Monetary)
RFM analysis is one of the simplest and most effective ways to segment customers based on profitability. You can run an RFM analysis in tools like Google Sheets or Excel. Check out this RFM template by Gala Ivannikova.
RFM analysis categorises your audience by scoring them on three criteria:
- Recency: How recently they purchased.
- Frequency: How often they purchased.
- Monetary: How much they’ve spent over a specific time period.
How It Works:
- Score customers on a scale (e.g., 1–5) for each factor.
- Combine the scores to create a final customer segmentation profile.
- Visualise results with a treemap to clearly see customer groups.
- Use these profiles to personalise your marketing.
Segmenting your audience is the modern approach to marketing. We now have the necessary resources and technology to achieve this. Get granular enough to identify significant groups and patterns, but too many segments will be difficult to manage and costly. There is a balance between being overly broad and being too precise with your groupings.
