Purpose
Traditional industry classifications weren't designed to capture how quickly private markets change. Companies increasingly span multiple categories, create highly specialized products, and reshape markets long before new industries are formally recognized.
The Crunchbase Micro-Industries Model organizes companies into highly granular market segments based on the products and services they offer. The model operates in two stages. First, proprietary clustering techniques identify groups of companies and products that naturally belong together. A targeted large language model (LLM) refinement layer then improves the quality and consistency of the resulting Micro-Industries, while structured human review helps ensure they continue to reflect how markets evolve. Because companies often participate across multiple markets, organizations may belong to more than one Micro-Industry.
Crunchbase’s continuously evolving micro-industries power Market Insights, providing directional summaries of how private markets are developing. Unlike static industry classifications, these AI-generated segments dynamically adapt to market activity — helping teams discover emerging markets, identify companies within them, and make more informed strategic decisions.
What the Model Evaluates
The Model is powered by Crunchbase’s proprietary Products and Services dataset, which spans more than 2 million private companies and 15 million mapped products — enabling micro-industries to organize private companies into dynamic market segments structured around actual company offerings rather than generalized industry groupings
Micro-Industries also provide the segmentation layer behind Crunchbase Market Insights. By analyzing activity across companies within each micro-industry, Market Insights identify broader patterns that help explain how markets are evolving.
Market Insights are generated by analyzing signals across the following categories:
- Micro-Industry Activity
- Funding Activity
- Exit activity
- Proprietary Crunchbase Engagement Data
- Market and Industry Context
By aggregating activity across entire Micro-Industries rather than individual companies, Market Insights uncover broader patterns that are often difficult to detect through company-level analysis alone — providing earlier visibility into where market momentum is emerging, accelerating, or contracting.
Model Performance
To validate quality and real-world market relevance, Crunchbase evaluates the Micro-Industries Model against manually reviewed benchmark labels spanning products, organizations, and market segments across a diverse range of industries and company types.
This evaluation measures whether assigned micro-industries accurately reflect real-world product markets while preserving relationships to broader industry categories. Assignments are evaluated based on parent industry alignment, product relevance, and organizational fit to ensure the highest-confidence micro-industries reflect how companies and products are positioned in practice.
The Model is evaluated on its ability to assign the most relevant micro-industries to each company. Precision measures how often the Model’s highest-ranked micro-industry assignments are correct, while recall measures how consistently it identifies the expected micro-industries for each company.
The performance metrics below summarize the Model's accuracy in assigning relevant micro-industries.
| Micro-Industry Assignment Performance | Result |
| Precision @2 | 84% |
| Precision @5 | 70% |
| Top-2 Recall | 82% |
These results demonstrate the Model's ability to consistently assign highly relevant micro-industries while maintaining broad coverage across specialized markets.
Crunchbase also evaluates product-to-micro-industry assignment through independent human review across a representative sample of products. Because product mapping forms the foundation of company-level micro-industry assignments, this evaluation provides an independent measure of the model's overall quality.
| Product Assignment Performance | Result |
| Product-to-Micro-Industry Mapping Accuracy | ~88% |
These results demonstrate the Model’s ability to deliver reliable and dynamic market segmentation that reflects how products, companies, and markets evolve in practice. Combined with Market Insights, the Micro-Industries Model provides a more complete understanding of how markets emerge, evolve, and converge over time — giving organizations earlier visibility into market change and a more modern framework for understanding how innovation moves across the private market.
Disclaimer
This content has been prepared by Crunchbase, Inc. (“Crunchbase”) for general informational purposes only. The information contained herein, including the outputs of the Micro-Industries Model (the “Model”), is not intended to be, and should not be construed as, financial, legal, investment, or other professional advice.
The Model generates predictions using automated machine learning systems and statistical analysis. Model outputs reflect probabilistic assessments and are not the product of individual human judgment or analysis. Model performance metrics presented herein reflect historical results evaluated against historical data and do not guarantee future performance. Actual results may vary materially from predictions due to changes in market conditions, data availability, model updates, or other factors.
In preparing this document, Crunchbase has assumed the accuracy and completeness of publicly available information and of other information made available to Crunchbase by third parties. Crunchbase has not conducted any independent investigation or verification of such information. No representation or warranty, express or implied, is made as to the accuracy, completeness, or reliability of such information, and nothing contained herein is, or shall be relied upon as, a representation, whether as to the past, the present, or the future. The information provided herein is not a recommendation to purchase, hold, or sell any particular security, nor does it constitute an offer or solicitation of any kind.
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