7 Types of Customer Segmentation Models 

1. Cluster Analysis Segmentation:  

Cluster analysis is a commonly used segmentation model that groups customers based on similar characteristics or behaviors. This model helps identify patterns within a diverse customer base. By employing algorithms that analyze data, marketers can create distinct customer clusters with shared traits. These traits could include demographics, purchasing habits, geographic locations, or other relevant data points. 

2. RFM Segmentation: 

Recency, Frequency, and Monetary (RFM) segmentation is a powerful method to categorize customers based on their transaction history. It assesses three key parameters: 

• Recency: How recently a customer made a purchase. 
• Frequency: How often a customer makes purchases. 
• Monetary: The total monetary value of a customer’s purchases. 

By segmenting customers according to RFM, marketers can target high-value, loyal customers differently from occasional buyers, enhancing personalization and engagement. 

3. Longevity Segmentation: 

Longevity segmentation focuses on the duration of a customer’s relationship with a brand. It categorizes customers based on their tenure, helping businesses identify loyal, long-term customers and newcomers. This segmentation model is particularly valuable for tailoring communication and offers to nurture customer loyalty. 

4. Combining Segmentation Models: 

Marketers often find value in combining different segmentation models to create more refined customer segments. For instance, merging high-value RFM segments with low longevity segments can result in a group of highly active, recently acquired customers. This hybrid approach provides a comprehensive understanding of customer behavior and allows for more precise targeting. 

5. Leveraging Machine Learning: 

In addition to traditional segmentation models, machine learning algorithms can be employed to discover new customer segments. Machine learning takes a data-driven approach, uncovering insights and groupings that might be challenging to identify manually. By utilizing advanced algorithms, marketers can enhance segmentation accuracy and discover hidden patterns within their customer data. 

6. Implementing Effective Customer Segmentation: 

Continuous Data Updates: Customer segmentation models must be regularly updated with fresh data to remain relevant. Staying current ensures that customer segments accurately reflect changing behaviors and preferences. 

7. Feedback Loop:  

Establish a feedback loop between segmentation models and campaign results. This iterative process allows for the refinement of segments and the identification of high-performing subsets within segments, optimizing marketing efforts. 

Conclusion: 

Customer segmentation is the cornerstone of effective marketing. While CLV remains a critical metric, various segmentation models, such as cluster analysis, RFM segmentation, and longevity segmentation, offer unique insights into customer behavior. Marketers can leverage machine learning for more sophisticated segmentation and improve accuracy over time. By continuously updating data and implementing a feedback loop, businesses can create highly targeted marketing campaigns that resonate with their diverse customer base. 

To learn more about how Optimove can take your customer segmentation to the next level, visit our Learning Center. 

Published on December 12th, 2023

Posted in

• Segmentation
• Personalization

Rob Wyse

Rob Wyse is Senior Director of Communications at Optimove. As a communications consultant, he has been influential in changing public opinion and policy to drive market opportunity. Example issues he has worked on include climate change, healthcare reform, homeland security, cloud transformation, AI, and other timely issues.