Customer segmentation

Definition

Customer segmentation is the practice of dividing a company’s customer base into distinct groups based on shared characteristics. These characteristics may include demographics, geographic location, purchasing behavior, needs, or psychographics such as lifestyle and values. Segmentation allows businesses to tailor marketing strategies, product offerings, and communication to better serve each group.

For example, a retailer may segment customers into frequent shoppers, seasonal buyers, and first-time purchasers. Each segment receives personalised offers that match their buying habits.

Advanced

Advanced segmentation uses data analysis, machine learning, and predictive modelling to create highly specific groups. Instead of relying only on demographics, businesses combine behavioral data, transaction history, and engagement metrics to build richer customer profiles. This leads to micro-segmentation, where even small groups can be targeted with precision.

Segmentation is often integrated with CRM systems, marketing automation platforms, and customer data platforms. Techniques such as RFM (Recency, Frequency, Monetary) analysis, clustering algorithms, and lookalike modelling help refine segments for targeted campaigns. Effective segmentation also supports customer journey mapping, retention strategies, and lifetime value optimisation.

Why it matters

• Improves marketing efficiency by delivering relevant messages.
• Enhances customer experience through personalised interactions.
• Identifies high-value customers for retention and loyalty programs.
• Helps businesses allocate resources effectively across markets.

Use cases

• Creating tailored email campaigns for different buyer groups.
• Designing product bundles for frequent or high-value customers.
• Segmenting by location for region-specific promotions.
• Targeting customer segments with personalised online ads.

Metrics

• Conversion rates by customer segment.
• Customer lifetime value per segment.
• Retention and churn rates across groups.
• Revenue contribution of each segment.

Issues

• Poor data quality leading to inaccurate segmentation.
• Over-segmentation making campaigns too complex to manage.
• Lack of alignment between segmentation and overall strategy.
• Privacy concerns when handling sensitive customer data.

Example

A subscription video service segments its audience into families, students, and professionals. Families are targeted with family plans, students with discounted offers, and professionals with premium features. This tailored approach increases sign-ups and reduces churn across all groups.