Firmographic datasets often contain hundreds of thousands—even millions—of companies. But standard industry codes only tell part of the story. A company labeled “Financial Services” could be a hedge fund, a payment processor, or a wealth management firm. These distinctions matter for market analysis, competitive intelligence, and targeting—yet traditional classification systems miss them entirely.
This is the problem that clustering and LLMs were built to solve.
In this blog, we’ll break down why combining clustering and classification produces better firmographic segmentation than either approach alone. We’ll walk through the methodology, show how Forage AI extends it with advanced techniques, and share a real-world case study where this approach helped segment over a million companies with 73% greater accuracy. We’ll also share what we’ve learned about making large-scale segmentation work.
Let’s start with the core challenge.
The Dual Challenge of Firmographic Segmentation at Scale
When working with firmographic datasets containing hundreds of thousands—or millions—of companies, traditional manual business categorization becomes impossible. The challenge is twofold:
- Volume and Variety: Processing information on vast numbers of companies with diverse attributes, geographies, and business models.
- Granularity: Identifying meaningful niche categories that standard industry classifications like NAICS and SIC miss entirely.
Solving both requires two complementary techniques: clustering and classification.
Clustering is an unsupervised machine learning technique that groups companies based on inherent similarities—without predefined categories. It discovers hidden patterns, identifies natural groupings across multiple variables, and reveals market segments you didn’t know existed. Clustering answers the question: what natural structure exists in this data?
Classification is a supervised approach that assigns companies to predefined categories based on training data. It applies consistent taxonomies across large datasets, ensures companies fit within established frameworks, and provides standardized categorization for reporting and analysis. Classification answers a different question: how do these companies fit into categories we already care about?
Neither technique alone solves the full problem. Clustering finds structure but doesn’t standardize it. Classification standardizes but can miss segments that don’t fit existing categories. Together, they let you discover natural groupings and make them actionable.
The question is how to combine them effectively. That’s where methodology matters.
The Methodology: Combining Clustering and Classification for Optimal Firmographic Segmentation
Step 1: Data Extraction and Preparation
Before any analysis can begin, comprehensive firmographic data must be collected and prepared. This typically includes:
- Company descriptions and mission statements
- Products and services offered
- Customer segments served
- Geographic presence
- Financial metrics and company size indicators
- Technological infrastructure and digital footprint
This preparation phase typically consumes 60-70% of the total project timeline—but it’s where accuracy is won or lost. Organizations that invest adequately in data preparation achieve classification accuracy rates 25% higher than those that rush this step.
With clean data in place, clustering can begin.
Step 2: Implementing Clustering to Discover Natural Groupings
Clustering algorithms identify natural groupings among hundreds of thousands of companies. Different methods suit different needs:
K-means Clustering
- Partitions companies into a predefined number of clusters based on attribute similarity.
- Best for: Large datasets where you have a rough sense of how many segments exist.
- Limitation: Requires specifying cluster count in advance.
Hierarchical Clustering
- Builds a tree of clusters, allowing you to choose granularity after the fact.
- Best for: Exploratory analysis where the right number of segments isn’t known.
- Limitation: Computationally intensive for very large datasets.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Groups companies based on density, automatically identifying outliers.
- Best for: Finding niche segments and unusual companies that don’t fit standard categories.
- Limitation: Requires tuning sensitivity parameters.
Clustering reveals structure—but it doesn’t standardize it. That’s where classification comes in.
Step 3: Applying Classification to Refine and Standardize Categories
While clustering discovers natural groupings, classification assigns companies to predefined categories—making the segments actionable for reporting, targeting, and analysis.
Random Forest Classification
- Builds multiple decision trees and aggregates their predictions.
- Best for: High accuracy with interpretable feature importance.
- Limitation: Can be slow to train on very large datasets.
Support Vector Machines (SVM)
- Finds optimal boundaries between categories in high-dimensional space.
- Best for: Datasets with clear separations between industry categories.
- Limitation: Requires careful parameter tuning.
Deep Learning Approaches
- Neural networks capture complex, non-linear relationships in text and structured data.
- Best for: Nuanced classification using unstructured data like company descriptions.
- Limitation: Requires more training data and computational resources.
Deep learning models enable 45% more granular firmographic segmentation compared to traditional statistical methods—particularly valuable when companies don’t fit neatly into standard industry codes.
Forage AI extends this methodology with additional techniques that capture nuances standard approaches miss.
How Forage AI Takes This Further
Beyond standard clustering and classification, Forage AI applies advanced techniques that improve segmentation accuracy—particularly for companies that don’t fit neatly into traditional categories.
NLP for Unstructured Data
Company descriptions, websites, and social media contain signals that structured firmographic fields miss. Our NLP-enhanced classification models deliver 56% improvement in identifying specialized business activities compared to models relying solely on structured data. This means catching distinctions like “PropTech advisory” vs. “traditional brokerage” that would otherwise be invisible.
Industry-Specific Classification
Standard industry classification codes like NAICS and SIC often fail to capture emerging sectors and cross-industry business models. Key approaches include:
- Custom taxonomy development for specialized industries
- Hybrid classification systems that combine standard codes with proprietary categories
- Dynamic classification frameworks that evolve with market changes
- Multi-label classification for companies operating across traditional industry boundaries
Here’s what this looks like in practice.
Case Study: Real Estate Firmographic Segmentation
A real estate firm approached us with the challenge of categorizing over 1 million companies in their database. Basic segmentation wasn’t enough—they needed to capture the diverse service offerings within the brokerage sector specifically.
Forage AI applied its clustering-classification methodology, powered by LLMs, to solve this.
The Challenge: Initial clustering algorithms successfully segmented the broader real estate ecosystem into approximately 100 distinct clusters based on business characteristics, operational patterns, and market positioning. However, within the “brokerage services” cluster, significant heterogeneity remained—traditional clustering couldn’t distinguish between retail brokers, investment sales brokers, tenant representation firms, and hybrid service providers.
The Solution: Forage AI deployed an LLM-powered classification layer on top of the clustered broker segments. By providing the model with detailed definitions of five core broker archetypes, the classification algorithm assigned multi-label tags to each company, recognizing that modern brokerages often span multiple specializations. For instance, a single firm might offer both industrial brokerage services and investment sales advisory.
The Results:
- 73% improvement in broker segmentation accuracy compared to single-method clustering, enabling precise identification of niche specializations like “tenant rep”, “landlord rep” and “investment brokers.”
- Multi-label classification revealed that 64% of broker firms operate across 2+ specialization categories, insights that pure clustering missed entirely.
- 81% reduction in manual research time for market analysts who previously spent hours categorizing broker service offerings through company website reviews and qualitative analysis
- Enabled identification of emerging hybrid broker models (e.g., firms combining traditional brokerage with PropTech advisory services) that didn’t fit conventional industry taxonomies.
Case Study: Investment Research
We applied this dual methodology for a financial services organization to help them identify investment opportunities across 300,000 private companies, resulting in:
- Identification of previously unrecognized industry micro-segments with high growth potential.
- 31% improvement in portfolio diversification through more precise industry classification.
- 43% reduction in misclassification of companies compared to traditional methods.
Projects like these have taught us what it takes to make large-scale segmentation work.
Challenges and Solutions in Large-Scale Company Segmentation
Delivering segmentation projects across millions of companies has taught us where these initiatives succeed or fail. Here’s what we’ve learned.
Challenge 1: Data Quality and Standardization
When working with hundreds of thousands of companies, data inconsistency becomes a significant hurdle.
Solution: Implement robust data cleaning pipelines with:
- Automated entity resolution to identify duplicate companies.
- Text normalization for company descriptions.
- Missing value imputation using industry benchmarks.
- Regular data quality audits and feedback loops.
Challenge 2: Computational Scalability
Processing data for hundreds of thousands of companies requires significant computational resources.
Solution: Leverage distributed computing frameworks:
- Apache Spark for large-scale data processing.
- Incremental learning approaches for continuous updates.
- Dimensionality reduction techniques to improve efficiency.
- Cloud-based infrastructure for scalable processing.
Challenge 3: Balancing Automation with Expert Oversight
While automation is essential for scale, human expertise remains crucial for validation.
Solution: Implement a hybrid approach:
- Active learning systems that flag uncertain classifications for expert review.
- Regular validation of clustering results against industry benchmarks.
- Periodic recalibration of models based on expert feedback.
- Transparent documentation of classification decisions.
Conclusion
Firmographic segmentation at scale requires both discovery and structure—clustering to find natural groupings that standard taxonomies miss, classification to make those groupings actionable. The combination, especially when powered by LLMs, delivers accuracy and efficiency gains that neither approach achieves alone.
Forage AI’s solutions apply this dual methodology to help enterprises segment millions of companies into meaningful, nuanced categories. Contact us to explore how this approach could work for your data.
