Top 10 Takeaways from the Data-driven VC Landscape 2023 - The TL;DR Version ;)

DDVC #35: Where venture capital and data intersect. Every week.

👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Thursday I cover hands-on insights into data-driven innovation in venture capital and connect the dots between the latest research, reviews of novel tools and datasets, deep dives into various VC tech stacks, interviews with experts and the implications for all stakeholders. Follow along to understand how data-driven approaches change the game, why it matters, and what it means for you.

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Thank You 🙏🏻

The past 48 hours were just mind-blowing🤯 Thank you so much for your incredible support in launching the Data-driven VC Landscape 2023! Although venture capital is such a niche industry, we made it into the top 10 of Product Hunt, got more than 100k social media impressions, saw over a thousand report downloads, welcomed more than 600 new subscribers 👋🏻, received hundreds of super kind DMs and range of collaboration requests for the 2024 episode.

I’m really overwhelmed and for me personally, it just shows how much of a topic the need for digitization in VC has become. In order to make the extensive document more digestible, I dedicate today’s episode to a “TL;DR version of the report”, so let’s focus on the major learnings below.

Top 10 Takeaways

1. “By 2025, more than 75% of VC investor reviews will be informed using AI and data analytics.” - Gartner

2. “While 84% of VC firms globally want to increase their efforts and resources to become more data-driven, only 1% of VCs have existing initiatives and dedicated teams today.”

3. “Efficiency: The productivity effect of using AI reduces the time to perform tasks by 0.8 standard deviations (SDs), increases output quality by 0.4 SDs, and reduces inequality between high and low-ability teams.”

4. “Effectiveness: Machine learning models have already been proven to outperform human investors in deal screening. In terms of accuracy and recall, they are on par with the best investors (80%) and relatively outperform the average/median investor by 40%/33%.”

5. “Inclusiveness: Talent is distributed equally, capital is not. There is a huge disparity in funding across the world, between 2017 and 2022, North American startups raised 30x, 60x, and 550x more capital proportional to population than MENA, LATAM, and Africa, respectively. Data-driven initiatives have been shown to help VC firms reduce bias and make better, fairer investment decisions.”

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6. “Younger and smaller firms more broadly build tools across the value chain than their older and bigger peers. Newer firms benefit from greenfield situations, whereas established VCs face more friction due to migration requirements, process changes, and cultural reluctance.”

7. “In terms of data-driven initiatives, sourcing and screening are the most penetrated stages of the investment process, closely followed by portfolio value creation and due diligence.”

8. “If you can’t measure it, you can’t improve it” - Start to track your miss rates to get a feeling about your actual coverage and prioritization abilities.

9. “Engineering activities scale well, strongly increasing assets under management (AUM) per engineer in larger funds.”

10. “Data-driven VC thought leaders have 3x more PhDs, 3x fewer MBAs, and 2x more STEM backgrounds than the average VC investor.”

I’ll keep it with this short episode for this week, to leave you enough time to dive into the full report, draw your own conclusions from the data, and share your thoughts with the community. Hopefully, by next year (click here if you want to participate in the 2024 edition), we’ll be able to provide even more quantitative insights on the actual impact of data-driven initiatives and see the two landscapes of firms and thought leaders grow through further adoption across the industry.

Stay driven,
Andre

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