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🌐 VC Deal Sourcing: A look at Traditional VC Sourcing.

Derek Watson
Derek Watson

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🌐 A look at Traditional VC Sourcing, the challenges and timeframes.

I wanted to share some thoughts and observations on deal sourcing as we move into a world where data and process-driven solutions will undoubtedly replace all repetitive human tasks and leave us doing just the stuff we love to do.

πŸ€” In the world of venture capital, the traditional approach to sourcing deal flow has been the same strategy for decades. Set in personal networks, serendipity, gut feeling, it presents unique challenges and time-consuming processes. Understanding these intricacies is key to appreciating the need for change and targeting how to move to more efficient, data-driven methods.

πŸ”— 1. Network Building

Timeframe: Ongoing; often a career-long endeavour.

Activities: Attending conferences, industry events, and networking meetups; making relationships with other VCs, entrepreneurs, and industry leaders.

Challenges: Time-intensive with no guaranteed outcomes. Relies heavily on the individual’s ability to network and build connections, potentially creating bias towards well-connected individuals and getting stuck in an echo chamber.

πŸ” 2. Deal Sourcing

Timeframe: Weeks to months; varies based on network efficacy.

Activities: Reviewing referrals from contacts, assessing leads from former colleagues, or considering pitches from existing portfolio companies.

Challenges: Can lead to an echo chamber effect where similar types of startups and founders are repeatedly favoured. Misses out on potentially amazing ideas that lie outside the VC’s network reach.

⏳ 3. Catchall to Initial qualification through manual screening

Timeframe: Forever.

Activities: Analysing pitch decks for thesis fit, primary assumptions based on pitch deck, initial meetings or calls with founders.

Challenges: Time-consuming to go through the main objective points which are so often correlated to other factors but the patterns are missed through a simple scoring methodology. This leads to a subjective qualification stage which is prone to personal biases and preconceptions. For example, founders with less charisma or weaker presentation skills, but with strong business ideas get overlooked.

So many data points that add value are missed through this manual process.

πŸ”Ž 4. Due Diligence

Timeframe: Weeks to months.

Activities: Deep dive into the startup’s founders and team background, problem and solution, market analysis, traction, competition, financials, legals, etc.

Challenges: Labor and time-intensive. Gathering and verifying information can be slow, and the in-depth analysis required at this stage often extends the timeframe for investment decisions.

As we have seen, due diligence (DD) also gets ignored, especially in “hot deals,” where the most underhand tactics of signalling are amplified, you know the ones, exclusivity, privilege, urgency, focus on wrong traction metrics, and the creation of a false sense of competition. These strategies often aim to rush investors into making hasty decisions without adequate assessment with the convenient truth of being able to hide behind power law (a few investments make all the returns)​​; it sometimes feels more like a casino than investment management.

🀝 5. IC Decision Making

Timeframe: Weeks to months.

Activities: Internal discussions and deliberations within the investment committee; revisiting findings from due diligence.

Challenges: Subject to groupthink and consensus challenges. Decisions can be influenced by dominant personalities within the committee rather than purely on the merit of the startup.

πŸš€ 6. Assisting in Portfolio Companies' Fundraising.

Timeframe: average 9 months to raise.

Activities: Guiding portfolio companies in refining their pitch, strategizing for fundraising, identifying potential investors, and leveraging the VC’s network for introductions.

Challenges: All the above, especially as it is limited to personal networks and serendipity. This stage is a reflection of the initial VC sourcing process, mirroring the challenges faced by founders.

Real-World Limitations of Traditional VC Sourcing:

We only have to think of a few opportunities to see the limitations of the current model.Β 

➑️Theranos highlights the importance of thorough due diligence, so many investors were misled by the hype of the company’s claims.Β 

➑️Sequoia Capital’s initial miss on investing in Airbnb and Dropbox shows the drawbacks of traditional deal sourcing, not being able to extrapolate data points or understand elasticity limits promising ideas.Β 

➑️Union Square Ventures’ success with Twitter demonstrates the advantages but also the exclusivity and limitations of tight and closed network-based sourcing, hardly helpful for the majority of VC’s.

This is what the average deal sourcing funnel looks like:

VC funnel based on 1,000 startups at the top:

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➑️ Initial Screening (1,000 Startups): Quick assessment to filter out startups that don’t meet basic criteria.

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➑️ Further Analysis (Hundreds of Startups): Detailed look at business, market potential, and traction, initial scoring and red flags. Start Founder calls.

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➑️ Due Diligence (Tens of Startups): In-depth review including financials, market research, and product evaluation.

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➑️ Investment Committee Review (10-20 Startups): Rigorous evaluation by the VC firm’s senior members.

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➑️ Final Decision and Investment (2-5 Startups): Negotiation and finalisation of investment terms.


The amount of this that could be, should be, will be automated and to a much higher level is insane.

πŸ’‘ All businesses will be disrupted by AI.

This traditional approach, with its long timeframes and inherent biases, underscores the need for a more streamlined and objective method. The reliance on personal networks and subjective decision-making has some merits but limits the scope of opportunities, overlooking innovative startups that fall outside the traditional radar.

πŸ“ˆ The next stage in the evolution of VC sourcing will be adopting a more data-driven approach through the entire funnel, it promises to mitigate these challenges by leveraging technology and analytics and for the early adopters the competitive advantage will make them industry leaders and the incumbents who believe if it ain’t broke, why fix it, left behind.

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