Startup Discovery & Sourcing

This month, PDL was thrilled to sponsor the Data-Driven VC Hackathon in Paris, where investors and data scientists from around the world explored how data can transform venture capital. At PDL, we believe human capital insights and B2B data are essential tools for every investor, and we saw some great examples of this from the participants and winners.

To share some of the hackathon’s insights, we’re creating a series of mini-recipes to help VCs and analysts tackle key challenges in startup discovery, market analysis, and portfolio management.

In this first post, we’ll focus on Startup Discovery & Sourcing, with three actionable recipes:

Identifying fast-growing and “trending” startups is essential for venture capitalists looking for promising investment opportunities aligned with their fund’s thesis. With PDL’s Company Search API and Company Enrichment API endpoints, you can filter and rank startups based on key attributes and important growth signals like industry, locations, headcount, employee growth rate and recent funding activity. This recipe illustrates an easy way to quickly target high-potential startups that align with your target industries and markets.

Identify the industries or geographic regions of interest (e.g., AI startups, European markets).

Use the Company Search endpoint to filter companies based on recent growth indicators like funding rounds, employee growth, or founding date. Include specific tags or industries in your search query.

From each returned company record, extract the key attributes like employee_count, founded, funding_rounds, and employee_growth_rate

Aggregate signals like employee growth rate, recent funding activity, and industry momentum. Create a scoring system to rank startups based on their relative or absolute growth.

Incorporate the ranks and scores of these startups into your investment strategy by aligning them with your fund’s thesis and objectives. Highlight high-ranking startups with standout growth metrics or strategic alignment for deeper evaluation and potential outreach.

For the most accurate trend analysis, consider combining growth metrics from PDL data with additional signals such as high-profile hires (e.g. former employees from top tech companies).

Instead of focusing purely on growth signals, enrich data for startups experiencing talent influx. Analyze high-profile hires alongside funding rounds and industry-specific tags to calculate a “talent momentum” score as part of your trend analysis.

Sometimes the best way to find new investment opportunities is to find companies right at the moment of inception. This recipe demonstrates how you can use PDL’s Person Search API and Company Search API endpoints to identify individuals who recently assumed founder roles and cross-reference their startups for indicators of early-stage activity.

Use the Person Search endpoint to find profiles with titles such as “Founder,” “Co-founder,” or “CEO” in their current roles. Filter by start_date within the last 6–12 months to ensure the roles are recent.

For the individuals identified in Step 1, use the Company Search endpoint to find their associated companies. Filter for startups with recent founding dates (e.g. within the last 12 months) and small employee counts (e.g. fewer than 5).

Optionally, evaluate founder profiles to confirm relevant skills, past roles, or entrepreneurial experience. Use the fields available from the person records returned in step 1 (like skills and previous work experience and education history) to assess the individual’s suitability for your sourcing goals.

Combine the insights from Person Search and Company Search to build a list of founders and startups that exhibit strong early-stage potential.

Incorporate the ranked list of founders and their startups into your startup sourcing strategy. Focus on high-promise candidates or ventures aligned with your goals, such as specific industries or geographies.

To uncover stealth or unannounced startups, search for profiles mentioning keywords like “stealth startup” or “building something new.” Additionally, enrich founder profiles to validate their entrepreneurial track record, such as past successful exits or notable roles.

If your focus is on identifying stealth projects, rely on person-level keywords and signals like their skills or industries of interest. Pair this with company-level insights, such as minimal online presence or placeholder websites, to identify startups still in their formative stages.

For any investment, it is important to contextualize startups against the competitive market landscape they operate within. This recipe walks through using PDL’s Company Search API to build a detailed sector overview of the key players and their equivalents within a market based on industry, tag and geographical attributes.

Identify the industry or market you want to map (e.g., “fintech,” “renewable energy,” or “AI”). Specify your industry requirements using PDL’s industry taxonomy alongside any tags you want to use in your query.

Use the Company Search endpoint to build a query based on the sector keywords. Apply additional filters such as founded (e.g., startups founded in the last 5 years), employee_count, or funding to refine your results.

Retrieve enriched records with key attributes like founded, industry, location, and tags. Use this data to gain insights into the size, distribution, and characteristics of startups within the sector.

Organize the data into a sector-specific map using a table, dashboard, or graph. For instance, group startups by sub-industry, geographic location, or funding stage to identify trends and clusters.

Analyze the resulting map to identify patterns, such as emerging sub-sectors, geographical hotspots, or startups with significant growth potential.

To deepen your analysis, consider incorporating dynamic signals from Person Search. Track talent movements, such as new roles emerging in sub-sectors (e.g., “AI ethicist”), to identify trends or new entrants before they’re widely publicized.

For advanced insights, use vector similarity searches on company descriptions to group startups based on textual similarities. This technique can surface hidden clusters or niche trends that keyword searches might miss, providing a richer understanding of the sector landscape.

We hope these recipes inspire you to take a more data-driven approach to discovering startups and uncovering hidden opportunities. Whether you’re searching for new opportunities to invest in, mapping out trends in an industry, or spotting promising new founders, these tools can give you a powerful head start.

In the next posts, we’ll dive into recipes across additional topics like Startup & Market Analysis and Portfolio Monitoring & Productivity - equipping you with actionable insights to streamline your workflow and make better investment decisions.

Got questions or want to share how you’re using these recipes? Let us know, we’d love to hear from you!

Stay tuned, and happy sourcing! 🚀