SMART Bayes Net Multi-Domain Segmentation: Uncovering Customer Complexity with Bayesian Networks

After more than 30 years in market insights and innovation across various industries, from consumer goods to fintech, I've seen a recurring challenge: segmentation efforts that fail to deliver their full value. Too often, they end up underused or fragmented. We've all seen it:

• Segments resonating in presentations but not translating to actual real-world activation

• Complex typing tools that gather dust

• Frameworks that create departmental silos instead of alignment

• Beautifully described personas lacking

• predictive value for key behaviors

  
  

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Traditional Segmentation: Powerful Tools, Important Caveats

Let's consider some widely used methods:

K-means clustering, a foundational algorithm, partitions data by minimizing variance within clusters. However, it tends to identify spherical and similarly sized segments. Customer behavior, with its inherent complexity and variability, doesn't always fit neatly into such predefined geometric shapes.

Hierarchical clustering creates a tree-like structure of nested clusters. While helpful in exploring different levels of granularity, this can impose a rigid, top-down (or bottom-up) structure that may not reflect the more fluid and interconnected nature of decision-making processes.

Latent Class Analysis (LCA) offers a more sophisticated, model-based probabilistic approach to identify unobserved (latent) segments from observed multivariate data. It's a step forward, particularly in its handling of categorical data. However, standard LCA models often assume local independence, meaning that, within a given class, the observed variables (attitudes, motivations, behaviors) are treated as conditionally independent. While these variables collectively define the classes, the direct, nuanced interdependencies between these input variables themselves might not be explicitly modeled as a network of influences.

Moreover, research and practical experience have shown that while demographic and psychographic segmentation provides valuable context, they can sometimes fall short in pinpointing the dynamic behavioral drivers of choice with the necessary precision to inform targeted interventions.

A Smarter Approach With Bayesian Network Segmentation: Probabilistic Models for Complex Decisions

At STRATEGENCE, we recognized these limitations and sought a methodology better suited to the complexities of human behavior. We've increasingly focused on Bayesian Network modeling for segmentation.

Here's what makes Bayesian Networks a compelling alternative:

• Modeling Probabilistic Relationships: Bayes Networks excel at representing and quantifying the probabilistic dependencies between a set of variables. Instead of just identifying correlations, they can be structured (often with expert input) to represent hypothesized causal relationships and influences. This allows for a richer understanding of how different factors interact.

• Handling Uncertainty and Incomplete Data: Bayesian methods are inherently probabilistic, making them well-suited for representing and reasoning with uncertainty, a constant in real-world data. They can also be adapted to manage missing data points more gracefully than some traditional techniques.

• Revealing Interdependencies: They explicitly map how variables influence each other, directly or indirectly. This network view is crucial because attitudes, motivations, usage occasions, and brand perceptions are rarely independent; they form a web of influences.

• Identifying Key Drivers and Simulating Scenarios: By modeling these interdependencies, Bayesian Networks can help determine which factors are most influential in driving specific outcomes (e.g., purchase, churn). They also enable "what-if" scenario testing, providing insights into how changes in specific variables might affect others across the network.

Real-World Applications and Results

We have successfully applied this approach across various clients and industries.

CPG Brand: An existing 10-year-old k-means segmentation was proving inflexible and difficult to apply consistently across brands and categories. By rebuilding with Bayesian Networks, we could model the interplay between motivations (e.g., convenience vs. indulgence), usage occasions (e.g., every day vs. special events), and brand perceptions. The resulting segmentation is now more robust and actionable across diverse categories.

PropTech (Real Estate Tech) Company: Previous behavioral segments struggled to predict actual home purchases. We developed a Bayesian Network model incorporating risk perception, life stage, financial literacy, and channel preferences. This approach provided a clearer view of the decision pathways, leading to improved media targeting efficiency and higher conversion rates.

FinTech Company: The company had defined segments for its digital wallet but lacked clarity on how to target and message them effectively. Bayesian Network modeling revealed key probabilistic relationships between factors such as digital trust, budgeting styles, specific app feature usage, and overall app loyalty. This analysis resulted in actionable targeting criteria based on observable behaviors, such as usage frequency and engagement with review platforms.

The Takeaway

Market segmentation is far from obsolete. However, the methods we use to define and activate segments must evolve to reflect the complexity of real-world decision-making. If your current segmentation isn't delivering the ROI you expect, or if it feels disconnected from the reality of customer decision-making, it might be time to explore approaches that are better equipped to handle the rich, interconnected nature of human behavior.

A well-designed segmentation should be a dynamic tool that drives strategy. Exploring methodologies like Bayesian Networks could be the key to unlocking that potential.

Let's talk. Book a complimentary strategy session here or contact me at rsilvestre@strategence-us.com