Contextual Classification: The Missing Layer in Business Intelligence

May 12

4 min read

“All models are wrong, but some are useful.” – George Box

Let’s say you’re helping a franchise expand into Kentucky. The goal: find a highway-adjacent location for a new coffee shop. You want to benchmark revenue, assess competition, and understand customer traffic patterns.

Simple enough—until you try to run that query through a traditional data stack.

What counts as a competitor? Just other coffee shops? What about bakeries with espresso bars? Should you include Dunkin' counters at gas stations? McDonald’s breakfast drive-thrus?

Every one of those follow-ups is a classification question. And every one depends entirely on context.

What Is Contextual Classification?

Contextual classification is the dynamic grouping of businesses, customers, or locations based on the goal of your analysis, where the boundaries shift to match the decision you’re trying to make.

Unlike traditional filtering or static industry codes, contextual classification adapts based on:

What question you're asking
What level of granularity is needed
How comparisons should be framed

Think of it like a zoom lens:

Center of the bullseye: Direct, highly relevant comparisons (e.g. independent coffee shops with drive-thrus near highway exits)
Middle ring: Businesses with similar customer flows or formats (e.g. quick breakfast stops with similar ticket sizes)
Outer ring: Broad category benchmarks (e.g. all limited-service food businesses across Kentucky)

This model evolves as your questions evolve. It’s not fuzzy logic—it’s structured adaptability.

And critically, contextual classification adjusts not just to the question, but to the available data. A question asked in Dallas, where there may be dozens of highly similar competitors within a mile, will use different classification boundaries than the same question asked in rural Wyoming, where business density is low and broader comparisons are necessary. LLMs shine here—they can shift the classification granularity based on sample size and data context in real time.

Why Traditional Data Science Struggled with This

NAICS codes and taxonomies aren’t broken—they’re structured and hierarchical. But they’re also static and assume you know the “right” classification before you start asking questions.

Earlier data science approaches reinforced this:

Analysts had to define cohorts manually
Models were built on fixed schemas
Follow-up questions (e.g., “what about just highway-only locations?”) required rework

As a result, the contextual thinking was offloaded to the human, who often had to navigate an overwhelming mix of data, dashboards, and edge cases.

How LLMs Flip the Script

Large Language Models (LLMs) are not a replacement for statistics or BI tools—they're a layer above them, helping structure questions and dynamically build cohorts without forcing rigid inputs.

LLMs are uniquely good at:

Ingesting messy, unstructured data
Inferring relevant groupings from intent
Updating groupings as new questions are asked

This enables fractal analysis—expanding only in the directions that matter as your understanding deepens.

(And yes, this is technically hard to build. Dynamic classification, transparency, and traceability aren’t trivial—but the framing shift is what unlocks the potential.)

Not Just Fancy Filtering

Filtering is static—you decide what to include or exclude. Contextual classification is responsive.

It lets you:

Shift your perspective in real time
Reweight relevance without needing to rebuild a query
Avoid starting over every time your question evolves

Say you ask: “How do coffee shops near I-75 perform in winter?” Then: “What if they also offer fresh baked goods?” Then: “Now compare them to fast food breakfast chains on nearby exits.” The classification updates each time—preserving context, but flexing scope.

This isn’t just more precise filtering. It’s an entirely different mental model—goal-driven exploration.

What This Looks Like in Practice

A franchise analyst enters: “Evaluate highway sites in central Kentucky for a new coffee shop.” The system identifies similar businesses with drive-thrus and breakfast traffic, benchmarks performance, and expands or refines cohorts as follow-up questions arise. The analyst toggles between local context and statewide trends, without breaking flow. The final insight isn’t just a list—it’s a narrative built interactively around the decisions that matter.

No code. No exports. No dashboards to decipher. Just context-aware insight on demand.

What This Means for Operators and Franchisors

If you’ve ever asked:

Where should I open next?
Is this market too crowded—or just the wrong format?
How should I define “my competition”?

Contextual classification helps you ask those questions without needing a data team or a stack of spreadsheets.

It turns your instincts into structured analysis—and lets the system evolve with your thinking.

(To the skeptical: no, it won’t make the decision for you. But it will dramatically sharpen what you're basing your decision on.)

But What If…?

“Isn’t this just benchmarking with a new name?” Benchmarking requires static cohorts. Contextual classification builds them dynamically based on your goal—and adjusts as that goal evolves.

“How do I trust it?” You’ll see how every cohort was constructed. Think of it like GPS—you can inspect the route, change the destination, or add detours at any time. You’re still in control.

“I already do this with my broker or team.” You do—but slowly. Contextual classification scales that thinking, updates it live, and makes it repeatable.

“What if I’m just starting out?” Start simple. Even thinking in layers—“What’s most relevant?” then “What’s broadly similar?”—is a step toward smarter decision-making.

Final Thought

Strategy lives in nuance. So does risk.

Rigid classification systems give you clean categories—but rarely the right comparisons. Contextual classification puts flexibility back where it belongs: in the hands of the decision-maker.

If we want data to help real businesses—not just generate reports—we need tools that can think like we do.