Understanding the universe of investment-ready, founder-owned companies is extremely complex. Private company data is spread across thousands and thousands of highly disparate, heterogeneous, and constantly changing sources.
Credit: carlos castilla / Shutterstock
Credit: carlos castilla / Shutterstock
Dealmakers need more than just company names. In order to find, qualify, prioritize, and engage targets, Private Equity, Venture Capital, Investment Banking, and Corporate Development need extensive profile data, accurate contact information and list sources. Validating, organizing, and continuously updating millions of data points requires a sophisticated combination of technology, people, and processes.
What problems need to be solved?
There are a broad set of uses for this data that are critical to the success of these businesses. Some of the most important include the following:
- Deal Origination - See more possibilities
- Firms need to find more companies that suit their strategy and aren't on everybody else's radar. Using a pure private company data set with rich signal information and tunable scoring allows for identification of hard-to-find deals.
- Target Outreach - Get there early
- It is critical to be a fast mover and beat your competitors in connecting with executive contacts using intelligence about thousands of conferences. Knowing who to connect with and where to find them keeps you a step ahead.
- Market Intelligence - Find the right deals
- Get smart on markets faster. There is a need for fresh, accurate and complete data from more than 115,000+ sources that need to be validated and organized into signal data for analysis and insights.
- Conference Planning - Make strategic moves
- Tracking conferences relevant to your investment strategy and knowing who to connect with and where to find them keeps firms a step ahead.
- Process Automation - Automate it
- Integration with existing CRM records and connecting dealmaking data sources is a time-consuming chore that leads to stale data and missed opportunities. Use a pre-built CRM integration and well-documented APIs to automate processes and data flows.
- Post-Transaction Monitoring - Amplify your deals
- Use your closed deals to build market momentum. Map markets to find comparable assets for post-transaction promotion and outreach.
- Portfolio Tracking - Get more from your portfolio
- Tracking private company performance and news is surprisingly hard. Firms must build portfolio sets and track them using fresh data and notifications.
- Advanced Analytics AI/ML - Build a Proprietary Data Advantage
- Each firm typically will have proprietary data that needs to be blended with external private company data sets to allow for advanced analytics driven by Artificial Intelligence and Machine Learning. Integrating with a full data warehouse or API integration can allow for a firm's internal team to have access to all the information they need.
What types of data and signals are needed?
Founder-owned, private company data can be challenging to source, but at SourceScrub, we have built our platform to focus on four key data dimensions.
- Companies:
- The company dimension captures core details on the company such as year founded, location, growth metrics such as employee count and job postings, and more.
- Sources:
- Sources capture where companies show up on the web. This includes buyer's guides, best-of lists, conference attendance, industry associations.
- People:
- The people dimension captures contact details and professional background of the people associated with a company.
- Investors:
- The Investor dimension captures information on the investors behind the companies. This includes transaction details, portfolio companies and deal history.
Within these data dimensions, there are several different signals that need to be tracked to support the use cases described. While there are hundreds of signals to choose from, we have built unique data processes around nine core signal categories. These signals allow you to make connections that accelerate your time to insight.
- Growth signals:
- Employee count, etc.
- Web signals:
- Search engine rankings, website traffic, etc.
- People signals:
- Board members, executive teams, etc.
- Investor signals:
- Company financing, investors, etc.
- Conference/trade show signals:
- Past attendance, planned exhibits, etc.
- Industry recognition signals:
- Won awards, inclusion in buyers’ guides, etc.
- Ownership signals:
- Ownership type, structure, etc.
- News and events signals:
- New hire announcements, media coverage, etc.
- Growth intent signals:
- New job postings, etc.
What solutions exist to solve these challenges?
Though there are many places to find these data and signals, SourceScrub is wholly focused on building and delivering products and solutions that address these use cases. Our products are tailored to firms' specific needs and workflows.
- Web Portal & Application
- Access data that has been categorized into 4 pillars: companies, people, sources, investments
- Easily cut through data through firmographic filters to find investment-ready targets
- Set alerts to get notified on important growth signals for companies you care about
- CRM Integration
- Integrate SourceScrub data into your CRM
- Enrich records and easily add records from SourceScrub into CRM
- Add contacts to company records in CRM
- Pull CRM fields into SourceScrub to filter through data
- Data Warehouse
- Access the entire SourceScrub data set
- Daily updates of all new data
- Detailed entity relationship modelling to optimize integration
- Unique contact metadata that is not in our other offerings (CEO scores, salary ranges, job postings, and more!)
- REST API
- Latest point-in-time data on companies
- Ability to pull all historical data for specific endpoints such as employee count, job count, contact information, and more!
- Ability to search through our complete list of companies and sources
- Allow users to retrieve all tags, delete, or append specific tags or subscriptions
What technology sets SourceScrub apart?
SourceScrub’s data engine and operations were built to solve the unique challenges of private company intelligence. They combine innovative machine learning with advanced web technologies and an 800-person data operations team. The result is the most accurate, complete, and fresh private company data set available.
Credit: SourceScrub
Credit: SourceScrub
Effective machine learning and artificial intelligence (ML and AI) require human-assured interpretation to accurately scale data aggregation and classification. SourceScrub's 800-person data operations team not only supervises the machine learning model to ensure data fidelity, but also interprets and hand-writes company descriptions so they depict the business more accurately than simply scraping website copy.
In addition to this continuous monitoring of quality, the data label provided by our research team feeds back into regular model retraining. On a regular basis, new models are trained and tested against our ground truth, and if they perform better, are deployed to our production environments to constantly maintain and increase accuracy. In an environment where new companies, segments, and trends are constantly changing, this ongoing supervision and retraining guarantees the highest quality and consistency for our end users.
Credit: SourceScrub
Credit: SourceScrub
- The author, Jon Dodson is CTO of SourceScrub.
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