What is Customer Segmentation? Examples and Methods

What Is Customer Segmentation? 

Customer segmentation is the process of separating an audience into groups of buyers with similar characteristics and preferences. As a result, brands can develop more targeted, influential interactions with current and potential customers, ultimately reaching those customers more effectively and increasing sales. 

Why Is Customer Segmentation Important? 

Customers expect companies to personalize their experience to target their unique needs and challenges. In fact, 91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them. 

Segmentation allows you to identify and combine similar customers into groups for more effective and targeted outreach, services, and renewals. It also can produce valuable data on customer needs that can be incorporated into companies’ overall strategies. With segmentation data, companies are 130% more likely to know their customers’ motivations and 60% more likely to understand their concerns and challenges. 

The Benefits of Customer Segmentation  

 Beyond driving effective outreach and service, customer segmentation can answer a wealth of key business questions, helping to inform strategies and enhance the customer experience.  

Critical Business Questions Customer Segmentation Can Answer:  

• What are the various customer segments in our market?  
• Who are the key decision-makers within each segment?  
• Which customer segments are most likely to purchase our products? 
• How would customers use our products? 
• What are the trusted resources or information services for our customers?  
• What types of information do customers seek when purchasing our product or service? 
• Which marketing channels are most effective to reach customers? 
• Which features or attributes are most appealing to customers? 

What Is an Example of Customer Segmentation? 

 Every company’s segmentation profiles will be unique, driven by the data they choose to collect and the answers their customers provide.  

For example, Sauer Brands built three distinct segments based on the ways their customers use different products, when they use them most, and what they use them for.  

The attributes companies look at to identify who is in the segment will often be different based on industry. The two examples below show how companies from two different sectors, a retail brand and a SaaS company, would prioritize different factors for segmentation.  The retail brand may choose to segment based on attributes like where and how they purchase, household income, or marital status, while a SaaS company would choose to segment based on attributes like role, company size, and business need.

B2C Customer Segmentation Example
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B2B Customer Segmentation Example

How to Collect Segmentation Data 

 The best way to develop accurate and actionable segments is to gather information directly from customers via a survey. Surveys allow businesses to develop targeted questions to identify the characteristics and values of their customers that can inform their strategies and then use data analysis to group responses together into meaningful segments.  

Surveys are also effective in overcoming organizational biases by providing customer-reported preferences, motivations, and needs that can differ from business assumptions or misinterpretations based on limited or selective data collection or inclusion. To be effective, the contents of your segment should be informed by the data you collect and the insights it provides.  

Collect customer insights and build effective customer segments for increased revenue and retention with Hanovers’ customer segmentation survey. 

What are the Customer Segmentation Methods?  

There are two primary methods of customer segmentation that can separate customers into distinct and effective groups.  

1. Cluster Analysis 
2. Decision Tree (CART) Analysis 

Type One: Cluster Analysis 

A cluster analysis is a process of grouping observations into classes that are similar within the set and dissimilar to observations outside the set. 

Data to Collect

Cluster analysis works best when using values-based data to inform the model. Values-based data include psychographic attributes, past behaviors, and decision drivers and tend to be dynamic and more likely to change.  

Psychographic data reflects a person’s beliefs and values and is measured through:  

• Hobbies  
• Interests  
• Attitudes  
• Beliefs  
• Relevant activities  
• Professional affiliations  

Behavioral data analyzes the activities, tendencies, and motivations of customers. Some data you can gather include:   

• Past interactions with your brand, including:  

• • Past purchases 
  • Engagement with your marketing channels  
  • Interactions on your website (e.g., downloading a white paper, adding a product to their shopping cart, etc.)  
  • Interactions with your employees (e.g., speaking with a salesperson, attending a product demonstration, writing a review, etc.)  

• Signals of disinterest, such as:  

• • Leaving a negative review   
  • Returning a product   
  • Switching to a new provider   

 
Decision-making drivers are data that uncovers the most influential components of your customers’ decision-making process, including where they are: 

• Price conscious  
• Reputationally influenced  
• Need proof of value  
• Level of service   

Cluster Analysis Example

The example below shows how plotting the responses from the survey revealed four distinct segments.

Type Two: Decision Tree (CART) Analysis   

When identifying distinct groups that are most or least likely to perform an action (e.g., purchase your brand), it is best to use a decision tree analysis. A Decision Tree Analysis separates your audience into discrete segments based on demographic and/or firmographic data and helps identify the characteristics of buyers who are most and least likely to make a purchase or complete the desired outcome. 

Data to Collect

Targetable characteristics for a decision tree analysis include demographic and firmographic data, which tend to be static and are less subject to change. 

Demographic data includes customer specific characteristics like:  

• Gender 
• Income 
• Ethnicity 
• Age 
• Location 

Firmographic data includes company and contact identifiers like: 

• Company Data  
• Company size 
• Company type 
• Industry 
• Geographic operations 
• Contact title  
• Contact department 
• Contact tenure 

Decision Tree Analysis Example 

The example below shows how the model separates the audience by the characteristics, resulting in “hot” and “cold” segments.