"When it comes to market segmentation, I don't see truly well-documented cases often. At a more simplistic level, we think of classic matrices such as BCG or McKinsey's. But the real exercise of segmentation is far more complex. In certain contexts, it comes close to the behavior of a tensor: multiple dimensions, cross-dependencies, distinct weights, temporality, and contextual factors that shift the meaning of data depending on the axis being analyzed."
"Thinking like a tensor is practicing Model Thinking, which remains, above all, an analog discipline. It requires a brain, not a machine. The challenge is necessarily multidisciplinary, and this is exactly where executives suffer, spending enormous time compensating for immature teams. Even when business operators manage to bring quantitative data from ERP, CRM, or sector reports (which are often scarce or methodologically fragile), the information set must be normalized."
"When unstructured data is added, the challenge grows further. This includes everything from more sophisticated sentiment analysis to qualitative inputs from field teams, customer recordings, or information mined from third-party sources. In these cases, the problem is not confined to normalization: It involves interpreting, validating, reducing noise, and converting natural language into structures that can interface with transactional data. It is epistemological, not just technical."
Market segmentation requires multidimensional modeling resembling tensor behavior, with cross-dependencies, distinct weights, temporality, and axis-dependent contextual shifts. Thinking like a tensor demands analog Model Thinking and human judgment rather than solely automated methods. The work is inherently multidisciplinary, forcing executives to compensate for immature teams. Quantitative inputs from ERP, CRM, or sector reports often need normalization and skills in statistics, data cleaning, sampling, dimensional modeling, and systems logic to prevent collinearity. Adding unstructured data—sentiment analysis, field inputs, recordings, third-party mining—introduces interpretation, validation, noise reduction, and natural language structuring challenges. Serious segmentation overlays many strategic layers to reveal true market positions.
#market-segmentation #multidimensional-modeling #data-normalization #unstructured-data #model-thinking
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