Customer Segmentation 101: from basic demographics to advanced behavioural clustering

Too many marketers just rely on basic demographic segments:

• Age groups
• Gender
• Location
• Income brackets

But these simplistic models miss crucial differences in behaviour and preferences. Two 35-year-old women from Melbourne may have completely different buying patterns and brand relationships.

Modern segmentation combines multiple dimensions to create more accurate, actionable customer groups.

Segmentation models compared

1. Demographic segmentation

Approach: Groups customers by measurable population characteristics.

Variables used:

• Age
• Gender
• Income
• Education
• Occupation
• Family status

Marketing application: Useful for initial broad targeting but lacks depth for personalisation.

Example: “Urban professionals, ages 25-34, household income £60,000+”

Strengths: Easy to implement, readily available data

Weaknesses: Poor predictor of actual purchasing behaviour

2. Geographic segmentation

Approach: Divides market based on location data.

Variables used:

• Country/region
• Urban/suburban/rural
• Postal code
• Climate
• Population density

Marketing application: Local targeting, seasonal promotions, regionalised messaging.

Example: “Coastal UK residents within 5 miles of retail locations”

Strengths: Enables location-specific marketing

Weaknesses: Assumes location determines preferences

3. Psychographic segmentation

Approach: Groups customers by psychological attributes.

Variables used:

• Lifestyle
• Values
• Opinions
• Interests
• Personality traits

Marketing application: Creating emotionally resonant messaging and branding.

Example: “Environmentally conscious minimalists who value experiences over possessions”

Strengths: Connects with customers’ core motivations

Weaknesses: Data is difficult to collect and validate

4. Behavioural segmentation

Approach: Groups customers by observed actions and interactions.

Variables used:

• Purchase history
• Brand interactions
• Product usage
• Loyalty status
• Buying stage

Marketing application: Targeting based on actual customer actions rather than assumptions.

Example: “High-frequency purchasers who buy premium products only during sales”

Strengths: Based on actual behaviour, highly predictive

Weaknesses: Requires substantial data collection

5. RFM segmentation (Recency, Frequency, Monetary)

Approach: Three-dimensional model based on purchase history.

Variables used:

• Recency: How recently did the customer purchase?
• Frequency: How often does the customer purchase?
• Monetary: How much does the customer spend?

Marketing application: Identifying high-value customers and those at risk of churning.

Example segments:

• “Champions” (recent, frequent, high-value)
• “At Risk” (not recent, previously frequent)
• “New customers” (recent, not frequent yet)

Strengths: Simple yet powerful, requires only transaction data

Weaknesses: Limited to existing customers with purchase history

6. Value-based segmentation

Approach: Groups customers by current and potential value to the business.

Variables used:

• Customer Lifetime Value (CLV)
• Acquisition cost
• Profit margin
• Growth potential
• Referral value

Marketing application: Optimising marketing spend and retention efforts based on customer value.

Example: “High-value, high-growth segment with 3x average CLV”

Strengths: Directly tied to business outcomes

Weaknesses: Complex to calculate accurately

7. Needs-based segmentation

Approach: Groups customers by the problems they’re trying to solve.

Variables used:

• Pain points
• Goals
• Purchase motivations
• Desired outcomes

Marketing application: Developing products and messaging that addresses specific customer needs.

Example: “Time-starved professionals seeking convenience over price”

Strengths: Customer-centric approach

Weaknesses: Requires qualitative research

Advanced segmentation techniques

Cluster analysis: Finding natural customer groupings

How it works: Uses algorithms to identify natural groupings in your customer data based on similarities across multiple variables.

Common algorithms:

• K-means clustering
• Hierarchical clustering
• DBSCAN (Density-Based Spatial Clustering)

Marketing application: Discovering non-obvious customer segments that might be missed through traditional methods.

Example discovery: “Cluster analysis revealed a profitable segment of weekend-only shoppers with distinct browsing patterns and high conversion rates for specific product categories.”

Predictive segmentation: Who will respond?

How it works: Uses machine learning to predict which customers are most likely to:

• Respond to specific campaigns
• Purchase particular products
• Churn in the near future
• Increase their spending

Marketing application: Focusing resources on customers with the highest probability of desired actions.

Example: “Top 20% of customers predicted to respond to our promotion, with estimated 4x higher conversion rates than non-targeted approach.”

Implementing effective segmentation

Step 1: Audit your data

Before segmenting, ensure you have:

• Customer attributes (demographics, preferences)
• Transaction history
• Engagement data (website, email, app)
• Customer feedback and survey responses

Step 2: Define your business objectives

Different objectives require different segmentation approaches:

• Customer acquisition: Focus on prospect segments
• Retention: Prioritise churn risk segments
• Up-selling: Target segments with growth potential
• Reactivation: Identify lapsed customer segments

Step 3: Build your segments

Start simple and iterate:

1. Begin with basic RFM segmentation
2. Layer in behavioural data
3. Add demographic and psychographic dimensions
4. Apply clustering for refinement

Step 4: Validate and test

Effective segments must be:

• Actionable: Can you reach them with targeted campaigns?
• Measurable: Can you track their response?
• Substantial: Are they large enough to justify targeted efforts?
• Stable: Will they remain consistent over time?
• Differentiable: Do they respond differently to marketing?

Common segmentation mistakes to avoid

• Creating too many segments: Focus on 5-7 actionable groups
• Relying solely on demographics: Behaviour is more predictive
• Static segmentation: Re-evaluate segments regularly as customers evolve
• Not acting on segments: Segmentation without targeted action is pointless
• Over-personalising: Balance customisation with efficiency and scale

Getting started with better segmentation

Begin improving your segmentation with these steps:

1. Implement basic RFM segmentation to identify your most valuable customers
2. Create different messaging for your top 3 customer segments
3. Test campaign performance across different segments
4. Build a progressive data collection strategy to enrich customer profiles