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Enhancing AI-based Investing with Public Web Data: Private Equity Case Study


Updated on Sep 14, 2021
Published on Sep 14, 2021
Blank image with text: "Private Equity + Coresignal"


Private equity firms rely on up-to-date data they can trust to capture a 360° view of companies and key players. Coresignal’s in-depth public web datasets, such as firmographic and employee data, help private equity firms enhance their data-driven decision-making. Recently, we spoke with one of our clients, a leading private equity firm, about how they are harnessing our public web data.  

This particular firm has been leveraging our datasets since 2019 and provided us with some insights on how they initially started utilizing public web data sources and how it’s been improving their investment intelligence processes. Let's take a closer look at how this PE firm utilized various types of Coresignal public web data sources to help enhance its investment strategies and overall performance.

Client Data sources Solutions
Private equity firm Employee data, firmographic data, community and repository data, and job postings data Signal generation, AI-based investing, investment analysis

"The primary use case that we had our eyes on when looking for an public web data provider was to better understand the network and how people move between companies."

Solutions and use cases

Below are some solutions and use cases utilized by this particular private equity firm.

Signal generation

As the investment market grows, investors can sometimes find themselves lost in a vast and competitive arena of startups and companies ready for additional funding. Specifically, job postings data and firmographics help private equity firms identify up-and-coming startups and other investment-ready companies. 

Our client was able to leverage our employee, firmographic, community and repository, and job postings data to enhance their signal generation, increasing the amount of data coverage they have on startups and growing companies. 

“We used public web data to create a data-driven approach that helps us find interesting investment signals.”

AI-based investing 

According to a 2020 McKinsey Review, the private equity markets added $4 trillion in assets in the past decade. As the private equity market continues to grow, it is more important than ever for investors to leverage AI-based investing technologies to stay ahead of the competition in an increasingly digital industry. 

With our high-quality public web datasets, our client can utilize AI-based technologies to identify key patterns. More specifically, they can fuel their investment models with our rich public web datasets. Their investment models and other AI-based algorithms help them predict market trends and identify insights and patterns about key players and decision-makers.

“With public web data, we can see how good our models are performing, in the sense that we get feedback from our users on companies they like and are interested in. When it comes to investment analysis, we're also supplying more personal insights for, say, the advisor network and end-users, which we couldn't even really do before.”

Investment analysis 

Private equity firms that implement data-driven investment strategies often rely on public web data to extract strategic or operational insights about startups. By analyzing information about a startup’s internal structure and operational tendencies, investors can better understand the companies they might invest in. 

In particular, our client can track growth and analyze the internal and operational structures of prospective and current clients. For example, employee and job posting data provide them with insights surrounding how a particular company’s teams change and evolve.

“Being able to look at the entire team and how that changes over time allows us to conduct a lot of analysis about the team and how that evolved, so we're able to see growth. That's one of the use cases that we found early from your data; that was really helpful.”


By utilizing our employee, firmographic, community and repository, and job postings data, our client could generate valuable market insights, fuel investment models, and reach their KPIs. Specifically, they complimented our fresh datasets, high-quality and in-depth coverage, and ready-to-use machine learning-friendly data. 

Fresh data 

With regularly updated public web data, investors can stay competitive and generate business opportunities in a fast-paced market. Our client understands the value of fresh data in the investment industry. For instance, fresh data allows them to measure “the number of investments happening due to our platform and the number of interesting deals happening that we flagged before they happened.”

High-quality coverage

Our high-quality employee and firmographic datasets include hundreds of unique data points from 20 business-related sources with the data originating from 190 countries. This richness and coverage guarantee a high degree of flexibility in how our clients can use the data. This specific client was able to leverage the richness of our datasets to enhance their signal generation, conduct high-quality market research, and get a better understanding of company and individual-level dynamics. 

Machine learning 

Many investors increasingly rely on up-to-date public web data to fuel their data-driven investing strategies. Particularly, our client was able to utilize our data “as input to our ML models which means that it is part of training and inference.” Ultimately, this process “functions like an AI advisor, providing insights on successful investment models and areas of interest in the market and to clients.”

“Ultimately, we used public web data to create a data-driven approach that helps us find interesting investment signals.”


In all, Coresignal’s public web data provided them with the “possibility to understand peoples’ opinions about companies” and gain a better understanding of movement and growth across multiple sectors around the world. Click here to learn more about how public web data can help you generate investment signals, build AI-based tools, and improve your overall investment intelligence.