What can you do with billions of web-sourced facts on hundreds of millions of organizations? Beyond analyzing the facts themselves, you (or a machine of your choice) can learn a lot. Historically, our Knowledge Graph has had one of the largest collections of publicly-disclosed organization revenue. Recently, we’ve applied machine learning processes across many org fields to estimate revenue for private organizations as well.
So why does this matter?
Revenue — even if estimated — helps to segment companies and estimate their profitability. Coupled with the myriad other firmographic fields in the Knowledge Graph you can search and segment unlike anywhere else online. To showcase the MASSIVE range of organizations for which we have estimated revenue, we created the visualization below.
Estimated Software Startup Revenue By US County
Data from Diffbot’s Knowledge Graph via this query in which we look at estimated revenue of software-as-a-service companies that are less than 2 years old. Our ML-enabled revenue field predicts revenue of hundreds of millions of private organizations by looking at a wide range of fields. The dataset for the below map contained over 10,000 software companies.
- San Francisco County, CA $257,519,000
- Los Angeles County, CA $227,248,000
- Kings County, NY $217,002,000
- Middlesex County, MA $167,717,000
- New York County, NY $141,814,000
- Dallas County, TX $130,535,000
- Worcester County, MD $110,000,000
- Santa Clara County, CA $100,064,000
- Broward County, FL $77,115,000
- Harris County, TX $75,842,000