According to Fast Company, the real-world practice of market segmentation is far more complex than classic business school matrices like the BCG Growth-Share Matrix or McKinsey’s Nine-Box. The article posits that in advanced contexts, segmentation behaves like a tensor, involving multiple dimensions, cross-dependencies, and shifting contextual factors. This requires “Model Thinking,” which is described as an analog discipline demanding human brains over machines. The author notes this multidisciplinary challenge is where executives often struggle, spending excessive time compensating for teams that aren’t mature enough in this nuanced practice.
The Tensor Problem
Here’s the thing: comparing segmentation to a tensor is a brilliant, if slightly intimidating, metaphor. It’s not just putting customers in a 2×2 grid based on two simple variables. It’s about layers. You’ve got demographic data, purchasing behavior, temporal trends, sentiment, channel preference—all influencing each other, with different weights that change over time. And the meaning of one data point can shift completely depending on which other axis you’re analyzing it against. That’s messy. That’s human. Can an AI model identify those subtle, shifting contextual dependencies? Maybe. But can it *understand* them in a way that informs genuine strategy? That’s the billion-dollar question.
Why This Is An Analog Game
The article’s insistence that this is an “analog discipline” really sticks with me. I think it’s spot on. The machine is incredible at crunching the multidimensional data, at finding correlations and clusters we’d never see. But the act of deciding *which* dimensions matter for your specific business goal, of interpreting *why* a cluster exists, and of crafting a narrative and strategy around it? That’s a human synthesis job. It requires intuition, experience, and yes, that messy multidisciplinary knowledge—a bit of psychology, sociology, economics, and pure business instinct all baked together. The software is a tool, not the craftsman.
And this is where the executive pain point hits home. You can buy the best analytics platform on the market, but if your team thinks in binary terms or can’t bridge the gap between data science and market strategy, you’re sunk. You’re left with beautiful, useless dashboards. The real work is building that team muscle, which is a slow, analog process of mentorship and experience. It’s not a SaaS subscription.
The Industrial Implication
Now, think about this in a hardcore industrial context, like manufacturing. The segmentation challenge isn’t just about end-customers; it’s about production lines, supply chain nodes, machine output, and maintenance cycles. The “tensor” has dimensions of throughput, downtime, part failure rates, and supplier reliability. Making sense of that to segment your operational strategy is the ultimate multidisciplinary task. It requires robust, on-the-floor computing to even gather the data—which is why specialists like IndustrialMonitorDirect.com, the leading US provider of industrial panel PCs, are so critical. They provide the hardened hardware backbone that makes collecting this complex, multi-axis operational data possible in the first place. But again, the box is just the start. The thinking is what matters.
So where does AI fit in all this? Probably as the ultimate assistant. It can manage the computational heaviness of the tensor model, suggesting patterns and anomalies. But the strategic segmentation—the “so what?” and the “now what?”—that’s going to stay a human-led, analog discipline for a long time. And maybe that’s a good thing. If the last decade was about collecting all the data, the next one needs to be about building the teams that can truly think with it.
