Using Public Web Data to Improve VC Deal Sourcing

I found public web data to be extremely useful and valuable in terms of improving VC deal sourcing. Essentially, there are three components to this strategy: generating a list of companies that fit your criteria with firmographic data, analyzing potential business founders with employee data, and discovering companies picking up traction, as well as some technographic information with job posting data.

In this article, I will cover each of those components and more to explain to you what exactly I have in mind and share some tips and tricks from my own experience with clients.

What are the different data categories public web data breaks down to?

First of all, the three most prominent categories of public web data are employee data, firmographic data, and job posting data.

Essentially, these data categories differ in data points and use cases.

Firmographic data consists of data points such as company name, location, revenue, address, headcount, and more.

Employee data consists of data points such as employee name, location, job title, experience, education, employment length, and more. In simple terms, it reveals professional information specifically about employees.

Job posting data consists of data points such as job title, location, employment type, open positions count, job description, and more. It shows you what employees a company is looking for. Job posting data also has technographic information in the description field.

Different ways to improve VC deal sourcing

Now that we know the differences between the datasets, let’s dive into the main part: how do you actually use them to originate deals?

Firmographic data: evaluating company parameters

The use of firmographic data for deal sourcing is pretty straightforward. It provides a base for the company valuation score with many variables, such as company size, location, revenue, funding, industry, and more.

So, for example, you could use firmographics to generate a list of companies that fit a specific criteria. If you’re looking for companies that have 11-50 employees, are located in Texas, operate in the IT industry, and haven't had a funding round in a while, you can do that with firmographic data.

However, to reap the most benefits of firmographic data, it’s recommended that you combine it with other data categories, according to Coresignal’s data expert Martynas.

Employee data: analyzing potential business founders

The standard practice entails checking whether the founder has had any experience with successful startups before. One rule of thumb is that if they have founded successful startups before, they might do it again. 

However, I found there to be 3 more innovative ways to use employee data for deal sourcing. Bear with me, this might just be what you were looking for.

1. Look for people with a specific job title in well-known companies that might start their own project

This strategy depends on what sort of potential founders you focus on in your research and discovery processes. Some people might consider marketing experts to be capable of building a successful business. Other people could have a product owner in mind. 

However, if you don’t have a clear-cut vision of what makes a potential startup founder, you could define a list of professionals that you think might have what it takes and check their experience and what they say about themselves on their public profiles. 

Once you define the criteria you’re going to use to identify successful founders, the process gets a lot easier. In this case, all you need to do is get some employee data and filter the database until you generate a list of prospective founders that fit your criteria.

For example, potential founders that have many years of professional experience and have worked in well-known companies, could earn some extra credit in your eyes and be placed above some other founders whose accomplishments are not as astonishing.

Another extremely valuable signal is when someone who has worked in a well-known company quits and does not go to work for another company. There is a good chance that they will announce the creation of their own startup.

And if you catch them starting a new project, you can reach out and discuss a potential deal.

2. Define a unique profile and reach out to those people without waiting for them to start a project

Another, more interesting use case I managed to find was that some VC firms don’t wait for the people mentioned above to start their project. Instead, they proactively contact those people and offer to provide funding and gather a team.

This way, the VC firm can overtake potential competition without even competing in the race. If you feel like you’ve got what it takes to prematurely identify a potential business owner that could build a unicorn, this might just be an ideal opportunity for you.

3. Evaluate the quality of the team working for a new startup

The main point here is that if a new startup managed to attract senior-level talent from well-known companies, there is a good chance that the new startup has something extraordinarily unique to make them quit their positions at well-established companies.

With employee data, you can map the movement of talent from one company to another and see whether new startups attract high-level employees from, let’s say, Fortune 500 companies.

If you find a startup like that, it’s most likely in your interest to dive a little deeper into its activities and ambitions.

Job posting data: anticipating growth and examining technologies and tools a company is working with

This last data category - job postings - is a bit more deceiving. Yes, it can be used to directly see how many job postings a company has put out to identify the company’s growth. However, I’ll show you that it can also be used in a smarter way.

Job posting data often consists of technographic data, too. In the job description, you can see what skills are required to apply. It shows what technologies and what tools the company uses on a daily basis.

It allows you to get two birds with one stone – not only do you get to see the expansion opportunities of the company, but also what technology and tools they use.

Should you use more than one dataset?

Generally speaking, yes. It’s better to combine several datasets for more accurate results.

However, it also depends on your goals. If you want to find the best companies to invest in, that requires having as much data about the company as possible. And to have that data, you will need to combine several datasets. Maybe even more than the ones I listed above.

On the other hand, if you only want to see what’s out there and simply feel the pulse of the market, maybe check the industry saturation, then firmographic data would most likely be enough.

To conclude

In short, public web data is one of the most useful information sources out there for deal sourcing. It provides you with many data points from companies to employees and job ads. The use cases vary depending on your creativity and goals. 

I’m sure that the ones I listed above are not the only cases where you can use data. If you know of another innovative use case, I’m always more than happy to hear about it. In case you have one, feel free to contact me at [email protected].