Customer Segmentation

Customer segmentation is different from filtering a list by a single attribute. For example, filtering customers in Hanoi creates a list based on geographic location. Segmentation goes further by combining location with purchase behavior, engagement, or needs to identify which groups require a distinct approach.

A useful segment usually has clearly defined criteria, enough customers for the business to act on, and a connection to a specific decision. If two groups have different names but receive the same message, priority, and service approach, separating them may not create clear value.

Why do businesses segment customers?

Segmentation helps businesses organize their activities around customer needs and value, rather than applying the same approach to everyone.

• Marketing: Tailor content, offers, and communication channels to different customer groups.
• Sales: Prioritize customers whose signals indicate a fit with the product or a need for direct consultation.
• Customer service: Choose an approach to support based on interaction history and customer needs.
• Resource allocation: Decide which groups need more consultation time, budget, or customer care.

Segmentation is a decision-support tool, not a fixed conclusion about an individual. Customers of the same age, location, or income level may still have different needs and behaviors. Similarly, high revenue does not necessarily mean high profit: businesses should also consider service costs, discounts, returns, and other relevant factors.

5 common customer segmentation methods

These methods are not mutually exclusive. Businesses can combine multiple criteria when the data is reliable enough and the combination serves a clear objective.

How do segmentation criteria differ from grouping techniques?

Criteria indicate which information a business uses to divide customers into groups. Examples include region, purchase history, or transaction value. Techniques are the ways a business applies those criteria to create groups.

Common techniques include:

• Rule-Based: Set explicit rules. For example, customers who have made at least three purchases within a given period are placed in the frequent-buyer group.
• RFM: Consider three factors: Recency (how recently a customer bought), Frequency (how often they buy), and Monetary (how much they spend). RFM can support grouping by purchasing activity; its calculations and thresholds should reflect the industry’s purchase cycle.
• Cluster-Based: Use an algorithm to find groups with similar characteristics in the data. K-means is a common example. It usually requires the number of clusters to be specified in advance and the data to be prepared appropriately.

The results of grouping depend on data quality and scope, variable selection, how scales are standardized, and how the results are interpreted in a business context. A cluster generated by an algorithm does not automatically become a useful segment. Businesses need to understand what the group means and how they can act on it.

The customer segmentation process in 6 steps

• Step 1: Define the objective. Clarify which business decision you want to improve, such as increasing engagement, prioritizing customer care, or allocating sales resources.
• Step 2: Collect relevant data. Choose the data needed for the stated objective. Sources may include CRM, transactions, websites, or interaction channels; you do not need to collect every type of data.
• Step 3: Clean and standardize the data. Handle duplicate records, missing information, or inconsistent formats. When combining sources, determine when records can appropriately be linked.
• Step 4: Choose criteria and create segments. Start with criteria that are easy to explain, then check whether the groups differ in ways that support decision-making.
• Step 5: Activate segments across Marketing, Sales, and customer service. Define a specific action for each group, such as adjusting content, outreach, or priority.
• Step 6: Measure and update. Track metrics tied to the original objective. Review rules and groups as data, products, or customer behavior change.

When handling personal data, businesses should consider the purpose and legal basis for processing, data subject rights, and current regulations in Vietnam. The specific application should be assessed in the context of the business.

What roles do CRM, CDP, and AI play?

• CRM manages customer information and recorded interactions across sales, marketing, or customer service. CRM data can support segmentation if it is relevant to the question the business needs to answer.
• CDP can connect data from multiple sources and support the creation of unified customer profiles when suitable identifiers are available. The ability to connect and unify data depends on the data, systems, and setup.
• AI can support pattern discovery, clustering, or prediction when the data and model are appropriate for the business problem. AI does not guarantee accurate results; predictions need to be validated against real-world data and assessed by the business. Not every CRM or CDP includes analytics or AI features.

Customer segmentation is also not the same as Customer Persona, Customer Profiling, Lead Scoring, CLV, or Customer 360. These related concepts answer different questions: segmentation divides customers into groups, while the others describe a representative profile, a customer record, potential, lifetime value, or a unified view of customer data.

Example: customer segmentation for an e-commerce retailer

This is a hypothetical example, not a standard that applies to every business. A retailer might organize its data into groups such as:

• New customers: Recently completed their first transaction.
• Loyal customers: Have made repeat purchases according to criteria set by the business.
• High-value customers: Have a transaction value or contribution that aligns with business goals.
• Low-engagement customers: Have reduced activity across channels over a period defined by the business.
• Promotion-sensitive customers: Often respond to offers, based on observed data.

A person can belong to multiple groups at once—for example, they may be both a loyal customer and promotion-sensitive. Businesses need to define how to prioritize actions when different groups suggest different approaches.

Common mistakes in customer segmentation

• Segments that are too broad or too small: Broad groups are difficult to personalize for; very small groups may not warrant a separate service approach.
• Relying only on demographics: Being the same age or in the same location does not mean customers behave the same way.
• Using inaccurate or outdated data: Incorrect information can place customers in the wrong group.
• Not connecting segments to actions: Creating groups without changing marketing, sales, or customer service approaches.
• Not updating segments: Behavior and needs change, so segments may lose relevance over time.
• Using data for the wrong purpose: Data use should be appropriate to its purpose and considered in light of current regulations.