👋 Hi, I’m Andre and welcome to my weekly newsletter, Data-driven VC. Every Tuesday, I publish “Insights” to digest the most relevant startup research & reports, and every Thursday, I publish “Essays” that cover hands-on insights about data-driven innovation & AI in VC. Follow along to understand how startup investing becomes more data-driven, why it matters, and what it means for you.
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I started this newsletter in September 2022 with a clear thesis in mind: VC is fundamentally broken and technology can help us fix it.
Initially, I shared a variety of learnings from my PhD research as well as my own journey at Earlybird including an overview of different manual and data-driven sourcing approaches, the “make versus buy question”, the most valuable commercial database providers, a step-by-step guide to scrape alternative data sources, the different approaches for de-duplication and entity matching, the importance of feature engineering for proper success scoring… and a lot more.
While diving deeper into this magic rabbit hole at the intersection of VC, data & AI, I occasionally shared some technically rather high-level but still related experiments such as my 10x productivity guide with ChatGPT or a process overview for augmented VCs that attracted surprisingly much attention.
20 months later, I took a step back and tried to understand why these IMO high-level posts resonated so incredibly well whereas some other IMO super valuable deep dives performed comparably bad.
Seeking Product-Market Fit
Assuming that post performance correlates with the value perceived by the readers (ceteris paribus), there must be a disconnect between the depth of the many deep dive posts I shared earlier last year and the level of technical sophistication of most VCs out there. Said differently, the further the distance between the level of sophistication of the reader from the level of sophistication of the content, the less valuable the content becomes and the worse it performs.
An analogy: If a Professor is supposed to learn from a primary school book, she’ll get bored right away. Little value. The same goes the other way around. If a primary school student is supposed to read academic papers, he’ll probably understand very little and at most takes away some inspiration. Again, little value. Both scenarios share that the level of sophistication of the content is too far away from the level of sophistication of the reader.
Yes, this insight seems obvious and is no rocket science but visualizing it helped me to make sense of the varying performance of different content pieces, i.e. my technically less sophisticated posts performed better than technical deep dives. With this rationale in mind, I tried to draw some conclusions about the VC audience and, more specifically, the status quo of the level of technical sophistication of VCs.
3 Stages of the VC Digitization Journey
The majority of VCs still live in a world with manual workflows, a simple tool stack with a generic CRM system like Salesforce, basic Email and note-taking tools like Apple Mail and Notes, Slack and/or WhatsApp for ad-hoc communication, Teams or Zoom for calls, G-Suite or MS-Suite for document storing and that’s more or less it. This is the “Old-school” extreme on the left side of the figure above where seemingly the majority of VCs are still stuck.
Next, we see the “Productivity VCs” in the middle of the figure above who are either setup with a modern off-the-shelf tool stack from day one or, and this seems to be the majority of VCs, firms that have been pushing for productivity and migrated from the old world. They successfully took the first leap and upgraded their “Old-school” stack with modern VC-focused CRM systems like Affinity or Attio, automated their workflows with Zapier, leveraged Notion for knowledge sharing and are hungry to explore further automation potential via tools such as ChatGPT.