Multi-level Customer Segmentation

At its core, segmentation is about grouping entities—customers, users, transactions, or products—into meaningful clusters based on shared characteristics.

Traditional segmentation often presupposes knowledge about group boundaries. For example, classifying clients as "Gold vs. Silver" relies on predetermined criteria. But what about the underlying behavioral patterns we might miss with such rigid categorization?

This is where data science-driven approaches come in. Instead of relying solely on predefined categories, we let patterns emerge from analytics and data. Clustering algorithms, statistical modeling, analytics and machine learning techniques help uncover hidden structures that may not be obvious at first glance.

For example, two customers spending the same amount might behave very differently—one might be a big amounts infrequent buyer, while another is a frequent shopper with low-margin purchases. A rigid ‘Gold vs. Silver’ label might treat them the same, but a data science driven approach can reveal deeper behavioral nuances.

Introducing the multi-level customer segmentation framework

After years of building customer segmentation systems, I've found that hierarchical multi-level frameworks consistently outperform single-layer and single-axis approaches across three critical dimensions: 

This approach consists of a horizontal and a vertical axis, and of three distinct layers.

The layers are:

• Macro Customer Segmentations
• Meso Customer Segmentations
• Micro Customer Segmentations

The axes are the methods we use to build the layers of our segmentation framework.

• Horizontal development is the primary approach for creating the macro segments (and some meso segments)
• Vertical development is the engine for creating the microsegments (and some meso segments)

In essence, the horizontal axis builds the 'what' (our strategic groups), while the vertical axis reveals the 'why' and 'how' within those groups, enabling targeted action.

Building the segmentations: horizontal and vertical axes

The foundation of scalable segmentation rests on recognizing that customer behavior operates across multiple layers of complexity. We need to capture the universal patterns that exist across our entire customer base and the nuanced behaviors that emerge within specific customer groups. Hence, the goal is building and understanding segmentation as a multi-dimensional architecture rather than a single classification system.

This dual requirement led us to developing frameworks where:

• Horizontal development establishes universal behavioral dimensions, while
• Vertical development creates specialized sub-segmentations within broader groups.

Horizontal development identifies behavioral dimensions that cut across the entire customer base. We think of these as the foundational layers that every customer can be mapped against regardless of their specific characteristics. These dimensions form the backbone of our segmentation strategy. Some examples of horizontal dimensions include: product affinity, channel preferences, and life cycle stage.

Vertical development takes a macro segment or a meso segment and creates more granular specialized sub-segmentations that capture unique behaviors within that customer group. 

The horizontal axis's goal is to maximize exhaustivity; ensuring we can classify every customer along fundamental behavioral dimensions. The vision here is creating a comprehensive map of our customer landscape that enables strategic decision-making and resource allocation. 

The vertical axis's goal is to maximize depth; it enables the personalization and specialized engagement strategies that drive tactical results.

Here is an example to illustrate this dual developement:

Fig1. An illustration of multilevel segmentation along horizontal (exhaustivity) and vertical (depth) axes.

A strong horizontal segmentation framework is about designing canonical dimensions, those that can consistently explain variance across the entire population. The key is not necessarily to build the most predictive dimensions, but the most universal and reusable ones.

These dimensions should be:

• Business-aligned: Dimensions must be aligned with key business model indicators and strategic levers (e.g., lifetime value, churn, product mix).
• MECE (Mutually Exclusive and Collectively Exhaustive): While orthogonality is rarely perfect, minimizing redundancy ensures that each axis adds unique explanatory power.
• Stability: Dimensions should stay consistent over time and resilient to noise, allowing for longitudinal tracking and strategic planning.

A sample of approaches for horizontal development might include:

• Heuristic-based or rule-based modeling to encode domain knowledge or business rules that capture known customer patterns.
• Factor Analysis or Principal Component Analysis (PCA)
• Predictive modeling to compute scores for churn, CLV, or purchase intent.
• Embedding techniques (e.g., Word2Vec on product interactions) to learn latent affinities.

To develop the vertical axis, a shift in mindset is required. We start thinking about specificity and heterogeneity rather than universality. The goal is to uncover meaningful differences within an existing horizontal segment.

Effective vertical segmentation requires that the sub-segments are:

• Differentiable: The sub-segments should exhibit distinct behaviors or characteristics.
• Actionable: You should be able to target each sub-segment with a specific, tailored action (e.g., a marketing campaign, a product recommendation, a customer support intervention).
• Impactful: The segment must represent sufficient value to justify the effort of targeting it. For broad campaigns, this may still imply a large number of customers. However, for highly granular micro-segmentation or nano-segmentation, impact could be defined by the high value of a very small segment (e.g., "users who abandoned a cart worth over $500") or even a "segment of one" triggered by a specific behavior in real-time.

Approaches for vertical development often include:

• Cluster analysis
• Propensity modeling
• Real-time behavioral triggers
• Autoencoders for latent feature discovery
• Unsupervised sequence modeling

Operating the segmentations: from model to system

An operational segmentation system supports decision-making, automation, targeting, and measurement across product, marketing, sales, and service. It should function as a reusable layer in the data architecture (i.e. always on, always improving.)

What typically happens is: your data science team builds an elegant segmentation model that performs beautifully on historical data. Stakeholders love the insights. So you deploy it to production, and within weeks, you're dealing with:

• Marketing campaigns targeting outdated segments because the model hasn't refreshed in three weeks

• Customer service agents who can't access segment information when they need

• Product teams building features for segments that no longer exist

• Executives making strategic decisions based on stale segmentation reports

The problem isn't the modeling, it's the operational layer. This is why we need to engineer the segmentation system for reliability, not just accuracy; which will require discipline across multiple stages of the system's lifecycle: conception, design, implementation, operation, and evolution. 

We start with a clear definition of how each business function (marketing, product, customer support, finance) will use segmentation outputs. This leads to a clear initial set of agreed-upon technical requirements, such as: refresh rates, interfacing needs, timing and latency. 

So the first thing we need to be on the look-out for are functional requirements. As explained, functional requirements -> technical requirements.  However, we need to also keep in mind that segmentations (especially on the micro level) serve as a discovery tool, in this case the data scientists need to propose an initial set of functional requirements to accompany the segments. These aren’t rigid production specs, rather they act as provisional “functional requirements,” later formalized once the micro-segments mature into production-ready ones.

To illustrate this, here is a side by side comparison of what functional requirements can sound like in these two different contexts:

I hope this illustrate how even though the data science team's functional requirements  are not system-level requirements, they guide model design, validation, and initial logic; functioning as proto-requirements.

Moving on ! once our requirements are defined and adjucated, we can move to the system's architecture. 

I see roughly three layers for this system: a data processing layer (data enrichment,  data quality, data integrity, feature engineering), a segmentation logic layer (versioned independently) and a service layer (APIs, connectors, dashboards.) Each layer is a subsystem that should be testable, observable, and replaceable without impacting downstream systems.

The data processing and service layers are more or less similar to layers found in other data project architecture. The segmentation logic layer should follow and implement MLOps principles such as version control, retirement policies for deprecated segments, evaluation workflows and feedback loops (include stakeholders in the verification process to ensure segments deliver on their intended value.)

Also, we mustn't forget to build fallback mechanisms that use cached segment assignments or rule-based logic when ML-driven segmentations fails. Better to serve a slightly stale macro segment than to serve no personalization at all ;)

Closing Thoughts: Segmentation is a customer understanding engine

A multi-level segmentation framework isn’t a one-off project. It’s an ongoing strategic capability that evolves with your customers and your business, unlocking sustainable competitive advantages across product, marketing, sales, and service domains.

If you're ready to implement multi-level customer segmentation in your organization, start with these three concrete steps:

1. Map your current segmentation landscape: What segments are different teams already using? Where are the gaps between strategic thinking and tactical execution?
2. Define your horizontal dimensions: Identify 2-3 universal behavioral dimensions that cut across your entire customer base and align with your core business functions.
3. Build your operational foundation: Before creating sophisticated micro-segments, ensure you have the infrastructure to refresh, serve, and monitor segmentation outputs reliably.

@article{nnz2025multilevelcusseg, 
 title   = "Multi-level Customer Segmentation",
 author  = "NNZ",
 journal = "nonneutralzero.com",
 year    = "2025",
 month   = "March",
 url     = "https://www.nonneutralzero.com/blog/analytics-data-6/multi-level-customer-segmentation-23"
}