Published

March 7, 2024

Targeting the “Most Likely” Investors

A data-driven approach can help identify the "most likely" investors for your specific startup

Founders
Investment
blog_author-avatar
Usman Gul
Founder/CEO, Metal

Most venture firms have a very specific stage at which they feel most comfortable investing. As a founder, my first challenge is to identify investors that are “most likely” to invest at my specific stage.

In finding early customers, startups need to be super targeted – we need to form a PoV on prospective customers that are most likely to buy our product. And the best founders are often fairly opinionated about their early customers. The same approach applies to fundraising.

Founders need to take the time to figure out which investors are the “most likely” candidates for a given startup. Some investors prefer to follow trends while others do not. Some prefer to invest at the earliest stages while others like to first analyze operating and revenue data. The most efficient way to determine the most likely investors is to look at prior investing patterns.

Let’s look at an actual example.

The chart on the left shows a breakdown of First Round’s investments by stage over the past 12 months.

While First Round occasionally participates in pre-seed and Series A rounds, their “sweet spot” is at seed. It is at the seed stage that they are most likely to invest.


Founders can pull together a list of all VCs that have made 20%+ of all portfolio investments at a specific stage. These are the investors that have a sweet spot that aligns with a given stage. After identifying VCs that specialize in my stage, I now need to run a number of qualifying checks.

As the first qualifying check, I want to identify VCs that are actively deploying capital. This is best achieved by placing a minimum 3-month deal count filter of "1" and then sorting the list based on the highest number of 3mo_deal_count. 

The above eliminates all investors from my list that have not made a single investment in the past three months (I.e. are not investing actively). And by sorting the remaining list for the highest 3mo_deal_count, I can identify VCs that are aggressive in their approach toward capital deployment.

An investor that makes one investment every quarter has a very different risk appetite versus one that makes 10 quarterly investments. Generally speaking, investors that are deploying capital most actively also tend to have the highest openness to see potential possibilities.

As the second qualifying check, I want to identify VCs that are open to investing in my geography. This is best achieved by filtering for investors that have made at least 5% of their portfolio investments in my continent, or at least “1” investment in a set of countries that is similar to the one I operate within. This may be most important for founders serving customers in developing countries.

As the third qualifying check, for each investor, I want to ensure that I am targeting investors that are somewhat relevant to my sector. For this, I take a quick look at the sectoral breakdown of each qualifying investor’s prior investments to ensure that they are either sector agnostic and/or have made significant investments in my sector (SaaS).

As of February (2024), there are 2,600+ active venture firms that have made at least “1” investment in the past three months. The exact process of identifying the most likely investors will vary for each founder, depending on the nuances of their company. What remains constant, however, is the process for using data to identify the most likely candidates.

Founders can meaningfully increase their odds of success by using data to identify a cohort of 50-100 investors that are the "most likely" candidates for their stage, sector and geography.