Beyond the Obvious: How Data Can Generate Pre-Seed Deal Flow Without Venture Studios or…
Beyond the Obvious: How Data Can Generate Pre-Seed Deal Flow Without Venture Studios or Accelerators
As a data-driven VC, I always believe that data can tell a story, and within that story lies the answer to seemingly impossible questions. One such question I’ve been thinking about is: how can pre-seed VC firms create new deal flow without being a venture builder, having a venture studio, running an accelerator, or having an entrepreneur in residence program?
To think outside the box, I decided to let the data speak to me. The journey started with a search on LinkedIn for people who have the word founder or co-founder in their current titles, and Careem as a past company. Essentially, I wanted to see how many founders came out of Careem, one of the most successful startups to emerge from the MENA region. My search led me to 362 results!
These individuals had all, at some point, worked at Careem and had now moved on to build their own companies. Some had gone directly from Careem to launching their startups, while others took different journeys — whether moving to other companies or going back to school — before becoming founders.
I did the same search with Talabat: 128 results, Foodics: 38 results, Anghami: 24 results, and even Tamara: 11 results.
If you search for more, and dig deeper into the journeys of those individuals, you start to notice fascinating patterns. Some trends stand out: what roles they held, how long they worked there, and what they did before joining. Did they come from a corporate background or a consulting firm? Had they started or worked at venture-backed startups previously? What about their education — where did they study, and which cities did they live in?
If you collect the data from LinkedIn alone, you can mine it and find even more trends. Ask a data scientist to help in this, and I’m sure he or she will be able to summarize surprising insights and stories from the data. Data science can reveal complex relationships and patterns that may not be immediately obvious. Machine learning techniques, for example, can cluster individuals with similar career paths or identify key predictors of future founders.
By mapping the career trajectories of individuals coming out of unicorns or companies that experienced successful exits, we can begin to recognize potential founders who may already be working on promising ideas but haven’t reached out to angels or pre-seed VCs yet.
Behind every exit and unicorn, there’s an incredible pool of talent that might just hold the next wave of startups. And by exploring the data behind these people — where they’ve worked, what experiences they’ve gathered, and where they’re heading — we can uncover potential deal flow, even without running a venture studio or an accelerator.
As a pre-seed VC firm or an active angel investor, if you conduct this analysis, you can then identify dozens of potential founders, reach out to them, invite them for coffee or lunch, to either seed their companies if they have something already, or seed the relationship for future ideas.
By leveraging data science and being proactive in building relationships, we can discover and engage the next generation of founders before they even enter the spotlight. Ultimately, it’s about staying ahead of the curve and nurturing relationships that could blossom into tomorrow’s success stories.
