How data science helps VCs optimize deal sourcing

Data from our 2024 investment predictions report shows that deal sourcing remains a priority for VC firms, but it’s becoming more complex and time-consuming—with investors using a variety of data sources and spending 10 more hours per deal on sourcing than the previous year. 

To keep up with the growing complexity of deals and the growing volume of datasets, VC firms are looking for ways to integrate data-driven approaches more seamlessly into their investment process. Firms are assessing and benchmarking their data needs, experimenting with existing tools, and identifying where data is most impactful in their value chains. 

While these steps are crucial in becoming more data-driven, ultimately, one of the highest impact decisions you can make is to seek data science expertise. This article explores the numerous benefits of integrating data science into your deal sourcing process, from helping identify outlier investment opportunities to driving consistent deal flow.

Deal sourcing is becoming more complex

PitchBook estimates that $201.8 billion was spent on the data analytics market in 2023, which they expect to grow at a compound annual growth rate of 18.1% over the next two years. 

Data is also becoming more decentralized—with many firms using multiple systems, sources, and data vendors (from Pitchbook and Dealroom to LinkedIn) to research a company’s headcount, funding rounds, existing investors, and other key metrics. 

Our 2024 investment predictions report shows that almost one-third of VC firms used more than 7 data sources to source deals in 2023. Max Eagle, Head of Data at WiL (World Innovation Lab), echoes this sentiment, “The biggest challenge to hit us when we started was not having a single source of truth.”

With more data complexities, VC firms need a way to structure, manage, and interpret data—to make data work for them, instead of being overcome by it.

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The role of data science in venture capital

Microsoft defines data science as combining “multiple disciplines to extract knowledge from massive datasets for the purpose of making informed decisions and predictions.” In short, data science is the practice of drawing insights from data. By applying data science to venture capital, VC firms can improve their investment decisions throughout the deal cycle—from the early stages of deal sourcing to due diligence and close.

Clifford Cohn, Investor at WiL (World Innovation Lab), describes why he hired his firm’s first data scientist: “The things we wanted to solve were: getting more processes and definitions around our pipelines and investment operations, and we also wanted to have [a] consistent owner or manager of our data infrastructure and data strategy.”

While hiring technical talent can be costly upfront, it can mean greater scalability and efficiency in your investment process over the long term and more opportunities for data-driven decision-making—ultimately providing a competitive advantage.

5 ways data science can optimize venture capital deal sourcing

From increasing your universe coverage to spending less time on manual data entry, employing a data scientist can ensure you’re focusing on the best investment opportunities and avoiding missing out on outlier opportunities, which can be costly for your VC fund.

Here are five ways data science can optimize your firm’s deal sourcing process and drive better investment decision-making: 

1. Increasing your investment universe

According to the data-driven VC, 79% of funds become data-driven to improve deal coverage and screening. And this is an area where firms are incorporating artificial intelligence and machine learning. For example, of data-driven VCs who use large language models (LLMs)—a specific type of machine learning model—in their value chains, almost 75% incorporate LLMs into their deal sourcing processes. 

Ray Zhou, Co-Founder and CEO of Affinity, says “Good deal sourcing datasets require exhaustive coverage. You need as much data as possible on your entire universe of potential startups and founders to ensure nothing gets missed.”

Data and automation enable VC firms to increase the sheer volume of investment opportunities by filtering through more data. The result is that investors can find and screen more startups and founders that fit their investment criteria faster, ultimately helping them make more informed investment decisions. 

For example, Clifford Cohn, Investor at WiL, adds “We are able—with new tools and data providers—to increase the universe that we’re actively looking in.” This also helps investors capture outlier startups and investment opportunities they would otherwise miss.

2. Monitoring data signals to identify opportunities earlier on

Data can act as a signal for identifying promising deals and sectors. By tracking the right metrics in the early stages of the deal lifecycle, you can discover and prioritize startups relevant to your investment thesis sooner.

Ray Zhou, Co-Founder and CEO of Affinity, recommends that VC firms should perform an assessment to determine the types of datasets most relevant to their theses. He says,  “Data-driven VCs should start by answering one fundamental question, ‘What data available in the world correlates with the signals most relevant to our firm?’” For example, consider metrics like:

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• Revenue growth 
• Employee headcount trends
• Leadership changes

You can then use changes in these metrics as trigger events to reach out to startups and see if they’re raising or looking for new partners.

3. Driving consistent deal flow

Implementing a deal sourcing process with the right balance of automation, tooling, and high-quality data can generate consistent deal flow. For example, 35% of data-driven VCs claim their data-driven tools are responsible for half of the deals they source today.

Max Eagle, Head of Data at WiL says, “We always know that investors will opportunistically discover deals either through other investors, their own research, [or] at conferences, and they’ll be adding them on their own. But this should be a small minority and not the majority of the time.”

To minimize manual input from investors, Max rebuilt a more robust infrastructure with advanced tools:

“On a daily basis, we are receiving new companies into Affinity and updates on existing companies—and we have several other jobs and processes that are ingesting data into Affinity in a similar way. Because we're getting high coverage, we're reducing the amount of time anyone is spending inputting information or researching information.”

The goal is to implement a sourcing process that maximizes investment coverage while minimizing manual input from investors. That way, venture capitalists spend the majority of their time where they can add the most value—evaluating opportunities, making investment decisions, meeting with founders and startups, supporting portfolio companies, and building long-term relationships. 

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4. Enhancing your VC firm’s operational rigor 

When Max Eagle joined to lead Data at WiL, his top priority was “getting alignment on the processes and definitions [used] across the team,” so that there was “a clear baseline for the investors to operate on.”

He started by assessing questions like:

How does our team review and monitor potential investments each week?

• How do our investors source new startups and how can we automate processes so they spend less time manually searching for opportunities?
• How do we codify progress on deals throughout the pipeline funnel so we understand where bottlenecks exist?
• How many investment opportunities in our investment universe are we capturing?

By evaluating existing processes and implementing new ones that are built to scale, VC firms can improve their efficiency and accuracy in sourcing deals and making more informed investment decisions.

5. Saving investors time with automation

The VC deal sourcing process typically involves manual data collection, like inputting company information, researching founders, and taking meeting notes. Data scientists can implement automation and processes to both reduce the amount of time investors spend on manual work and increase data accuracy.

Max Eagle says “One of my big motivating factors here [at WiL] was seeing how much time each of the investors was spending looking up information on companies rather than meeting companies and really diligencing them. I wanted to flip the order there.”

This is an area where tools can make all the difference. For example, Affinity saves firms over 200 hours per person each year on manual data entry by automating the creation of people and company profiles, enriching those profiles with real-time Affinity data and other third-party providers, and tracking deal teams’ engagement histories and relationship strengths. That way, investors can spend less time on manual data entry and more on decision-making, portfolio management, and building long-term relationships with key stakeholders. 

Make data your competitive edge

When used properly, data can serve as a significant competitive advantage in venture investing. Kamil Mieczakowski, Principal at Notion Capital, shares how data allows her firm to differentiate: “If you're a VC firm, it's a very competitive space, and everyone is trying to figure out what their edge is. For us, a core focus has always been around data, good coverage, and being very informed in the market rather than very reactive.” 

Data science is becoming increasingly important in venture capital because VC firms need to make sense of the noise—to evaluate data sources, rebuild faulty processes, and integrate new systems, all while fulfilling their core functions.

Affinity is excited to partner with leading venture firms on the future of data-driven deal sourcing and to continue building out this ecosystem together. As the CRM built for venture capital, Affinity combines artificial intelligence, automation, and deal data to provide data scientists with the infrastructure to drive efficiencies and empower dealmakers with the insights needed to source more high-quality deals faster. 

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