Unlocking the Secrets Behind 100+ Early-Stage Investments: What Drives Startup Success?

In venture capital, every decision—whether at the initial pre-screening stage or during post-investment portfolio management—can influence the trajectory of a startup. But how do early-stage investors decide which startups deserve their time? And once a company is funded, what drives its ability to scale—or fail?

At NuFund Venture Group , we pioneered the venture group investment model, where a collaborative network of investors pools resources through a venture fund. Since launching our first fund in 2019, we've backed over 100 companies across six funds. Last summer, we embarked on two major projects to improve decision-making at two critical stages of the deal funnel: early-stage screening and post-investment portfolio analysis.

Working alongside 美国哈佛大学 , 耶鲁大学 , and 美国加州大学圣迭戈分校 students, we analyzed our portfolio companies, interviewed stakeholders, and reviewed deal histories, investor updates, and the factors contributing to successful and failed investments. Through these efforts, we are gathering key insights to refine our investment processes, particularly by creating AI tools that will help us enhance pre-screening and due diligence.

Here's what we've learned so far.

What Drives Startup Success? Insights from Our Portfolio Analysis

In one of our projects, Kenan Pala (Yale) and Kardelen Kilic (Harvard) worked on analyzing success and failure factors across our portfolio. This analysis spanned diverse sectors such as biotech, SaaS, and cybersecurity. By examining over 100 deal memos, financial records, and investor updates, we better understood what leads to long-term success.

Some key patterns emerged:

• Adaptable Leadership Beats Static Experience: Our analysis reaffirmed that the most successful founders were those who could pivot effectively when market conditions changed. Startups with rigid leadership or a failure to adjust their product often struggled. This aligns with industry research, which stresses the importance of adaptability and responsiveness to market feedback. As Y Combinator's Paul Graham points out, the team behind the startup often matters more than the idea itself; adaptability and the ability to create something people want are crucial.1
• A Continuous Approach to Product-Market Fit: Product-market fit is often seen as a startup's Holy Grail, but it's not a one-time achievement—it's a process. Startups that consistently refined their product based on customer feedback and adapted their go-to-market strategies tended to thrive. On the other hand, those who failed to adjust after early challenges often experienced diminishing returns. Garry Tan from Y Combinator emphasizes that startups often fail when they can't find product-market fit, reinforcing the importance of iterative validation and continuous adaptation.2
• Strategic Pivots as a Key to Growth: Many early-stage startups lacked strong financial metrics like CAC (Customer Acquisition Cost) and LTV (Lifetime Value). In these cases, the ability to identify critical pivot points—whether in customer acquisition or sales strategy—was essential. Our new Deal Analyst AI tool, designed as a part of this project, supports this analysis by reviewing deal memos and identifying potential risks and opportunities before final investment decisions. Although the tool is still in its early stages, it will eventually help us ask more informed questions during due diligence and provide deeper insights into historical and prospective opportunities.3

Pre-Screening with AI: Optimizing the Top of the Funnel

Our second project, supported by Amin Haghani, Luai Al-Hinn, and Sooho Oh (and supervised by our NuFund Member Robert Hill), addressed the top of the deal funnel: pre-screening the hundreds of applications we receive. Although this project is ongoing, the early findings have been promising.

Here's what we've learned so far:

• Founder-Market Fit Is Key: A founder's experience in their target market is one of the strongest predictors of success. Our AI tools, currently in development, will soon help surface founders with the most relevant industry expertise, enabling us to focus on startups with a higher likelihood of success. The tools have also flagged cases where founders overstated their qualifications or lacked deep market understanding. According to Harvard's Paul Gompers, founder adaptability and alignment with the market are more predictive of success than financial projections.?
• Balancing Ambition with Realism: While founders often present bold, visionary plans, we have learned that clear market validation is often a better predictor of success than lofty projections. Companies that demonstrated early traction—through customer feedback, partnerships, or initial revenues—tended to perform better. CB Insights reports that 42% of startups fail due to a lack of market need, underscoring the critical importance of refining product-market fit continuously.?
• Mitigating Execution Risk: Our screening process aims to avoid startups that overpromise but underdeliver. While ambitious roadmaps are common, they can often mask execution risks. Our goal is to be able to flag startups whose operational plans seemed disconnected from reality, helping us prioritize startups with scalable, realistic growth trajectories.

Navigating the Complexity of Deal Funnel Phases

Although these two projects focus on different ends of the deal funnel, they complement each other in critical ways. The pre-screening AI tool will help us identify the highest-potential startups early in the funnel, enabling us to allocate resources more effectively. At the same time, the Deal Analyst AI tool will assist during the later stages by offering more granular insights into the opportunities and risks highlighted in our deal memos.

These tools will offer a more holistic, data-driven approach to our decision-making process. By optimizing early-stage screening and deeper due diligence, NuFund aims to be better equipped to prioritize screening efforts to the most promising companies in our pipeline. This combination of early-stage filtering and AI-enhanced analysis at the portfolio level will ultimately enhance our investment strategy.

Key Takeaways for Founders: What Investors Look For

From these projects, we've distilled actionable insights that can help founders navigate the early stages of fundraising:

• Prove Your Founder-Market Fit: Be clear about why you are the right person to lead your startup. Show how your experience directly applies to the problem you're solving. Investors look for founders who deeply understand their market, and overstating qualifications can lead to red flags.
• Adaptability Matters More than Perfection: Don't hesitate to highlight how you've adapted or pivoted based on feedback. Startups that demonstrate flexibility are more likely to secure funding than those rigidly following their original plan. Jessica Livingston of Y Combinator emphasizes that the best founders pivot quickly and adapt without being overly attached to their original ideas.?
• Show Real Traction: Ambitious plans are great, but investors want to see validated traction. Whether it's customer feedback, partnerships, or early revenues, demonstrating that you've tested your assumptions and can back them up with data will help you stand out.

The Road Ahead: Learning from Collaboration, Embracing Data, and Constantly Evolving

One of the most exciting aspects of these projects has been the opportunity to collaborate with talented students from Harvard, Yale, and UCSD. Their fresh perspectives have allowed us to uncover new insights and improve our decision-making tools. Even as a well-established investor group, we continue to learn from those just entering the venture capital field. These collaborations demonstrate that institutions with deep data and historical context can help make early-stage investing smarter, more efficient, and more inclusive.

The message for early-stage investors and founders is clear: stay flexible, stay data-driven, and stay open to refining your approach. Whether through technology or by leveraging collaborative insights, success in venture capital belongs to those willing to learn and evolve.

(Special thanks to Kenan Pala, Kardelen Kilic, Amin Haghani, Luai Al-Hinn, Sooho Oh, and Robert Hill & Ashok Kamal for their invaluable contributions to these projects.)

Footnotes:

1. Graham, P. (2009). Why to Start a Startup in a Bad Economy. Retrieved from https://www.paulgraham.com/badeconomy.html
2. Tan, G. (2020). The Importance of Product-Market Fit. Y Combinator Startup School. Retrieved from https://www.startupschool.org/videos/208
3. Wang, X., & Ihlamur, Y. (2024). Automated Startup Evaluation Pipeline: Startup Success Forecasting Framework (SSFF). Retrieved from https://arxiv.org/abs/2405.19456
4. Gompers, P., Kovner, A., Lerner, J., & Scharfstein, D. (2020). How Do Venture Capitalists Make Decisions?. Harvard Business Review. Retrieved from https://hbr.org/2020/03/how-do-venture-capitalists-make-decisions
5. CB Insights. (2021). Top Reasons Startups Fail. Retrieved from https://www.cbinsights.com/research/startup-failure-reasons-top
6. Livingston, J. (2016). Founders at Work: Stories of Startups' Early Days. Retrieved from https://blog.ycombinator.com/founders-at-work-stories