Get the big picture with historical data
- Quantify and evaluate company performance over time
- Enhance backtesting and business analysis processes
- Build and train predictive investment models

Up to 50 months of data
Raw data, detailed records
Data from 8 web sources
Historical data use cases
Building predictive models
With historical firmographic data, organizations are able to build and train predictive investment models with ML algorithms.
Backtesting and analysis
Companies are also able to leverage historical data for backtesting and analysis processes such as performance testing, fault testing, and integration testing.
Evaluating company performance
With over 649M+ professional profiles and 98M+ company records from a variety of sources, our data is ideal for quantifying and evaluating company performance.
Stay ahead of the game with fresh web data
Coresignal's data helps companies achieve their goals
Evaluate company performance with Historical headcount dataset
Quantifying and predicting company growth can be a challenging task. With Coresignal’s Historical headcount dataset investors can easily see the historical growth trends of more than 68 million companies worldwide. Enriched with thorough firmographic information, this data can be used to analyze company, industry, and location trends over time to enhance analysis and back decisions with hard data.
Coresignal's Historical headcount dataset includes continuously updated snapshots of headcount and social media follower trends, dating back to November 2018. For more information, download our free sample.

What is historical data?
Historical data is information collected about past occurrences, interactions, and outcomes surrounding a particular organization or entity. Historical data is leveraged by businesses, analysts, and investors to help build and train ML algorithms, to analyze time-series data for backtesting processes, to establish a baseline for conducting competitive analysis, as well as for overall market and industry trend forecasting.
Historical data for investors
Investors are able to leverage historical data to measure and predict financial success such as revenue and growth by tracking companies’ employee counts, such as employee distribution by function and seniority as a percentage of the total employee base.

Trend forecasting with historical data
Historical firmographic and employee data is used in statistical modeling to help organizations forecast company, market, or industry trends. Firmographic data can be used to forecast larger industry and market trends, while historical employee data can be used for company-level quantitative forecasting.

Capture a 360° view of companies and professionals with historical data
With Coresignal’s comprehensive historical alternative datasets, investors are able to capture a 360° view of companies and professionals. Our most popular historical data types include up to 50 months of employee data and firmographic data.
Employee data
General information
Job experience
Education
Skills and abilities
Interests and activities
Firmographic data
General information
Financial information
Location
Job listings
Reviews
Stay ahead of the game with historical web data
Coresignal's data helps companies achieve their goals
Frequently asked questions
What is historical data analysis?
Historical data analysis is the process of observing and summarizing market and company behavior over time.
What is the importance of historical data?
Historical data is important for businesses and investors interested in conducting competitive analysis, tracking growth rates, and market trends.
What is historical financial data?
Historical financial data is financial information about a particular company, organization, or investor that is analyzed in conjunction with past financial events, trends, results, etc.
What is historical social media data?
Historical social media data is past web information about particular companies and professionals collected from public social media sources.
How is historical data categorized?
Historical data is categorized into two categories: primary and secondary sources. Primary sources tend to be more qualitative and include first-hand accounts and documentation, while secondary sources are more quantitative and include reports, filings, statements, records, etc.